Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit
Abstract
:1. Introduction
2. Basic Models of Artificial Intelligence
3. Domains of Artificial Intelligence’s Applications in Neonatal Care
3.1. Neuromonitoring
3.1.1. Electroencephalography
3.1.2. Magnetic Resonance Imaging
Aim | References | Artificial Intelligence Model | Data-Set Analyzed | Outcome |
---|---|---|---|---|
Electroencephalography | ||||
Automated seizures detection | O’Shea et al. [76] | DL detection models based on SVM system and AUROC | Continuous EEG recordings | The system achieved a 56% relative improvement, reaching an AUROC of 98.5%; this compared favourably both in terms of performance and run-time |
Liu et al. [8] | SAM analysis | Continuous EEG recordings | SAM analysis demonstrated a sensitivity of 84% and a specificity of 98% in effectively differentiating between EEG epochs containing seizures and those without | |
Gotman et al. [9] | Spectral analysis | Continuous EEG recordings (281 h of recordings containing 679 seizures) | 71% of the seizures and 78% of seizure clusters were detected, with a false detection rate of 1.7/h | |
O’Shea et al. [77] | DL detection models based on SVM system | Continuous EEG recordings | The algorithm had an AUROC of 88.3% when tested on preterm as compared to 96.6% when tested on term EEG. When re-trained on preterm EEG, the performance increased to 89.7%. An alternative DL approach showed a more stable trend when tested on the preterm cohort, starting with an AUROC of 93.3% for the term-trained algorithm and reaching 95.0% by transfer learning from the term model using available preterm data | |
Pavel et al. [78] | Algorithm for automated neonatal seizure recognition | Continuous EEG recordings | Sensitivity and specificity were 81.3% and 84.4% in the algorithm group compared to 89.5% and 89.1% in the non-algorithm group, respectively; the false detection rate was 36.6% in the algorithm group and 22.7% in the non-algorithm group. The percentage of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (difference 20.8%) | |
Mathieson et al. [80] | SDA | Continuous EEG recordings | SDA achieved seizure detection rates of 52.6–75.0%, with false detection rates of 0.04–0.36 FD/h. Time based comparison of expert and SDA annotations using Cohen’s Kappa Index revealed a best performing SDA threshold of 0.4 (Kappa 0.630) | |
Severity grading of neonatal HIE | Stevenson et al. [79] | ML classifier models of AGS based on a multi-class linear analysis and AUROC | Continuous EEG and clinical data | The 4 grade AGS had a classification accuracy of 83% compared to human annotation of the EEG. EEG-only measures were shown to be less effective in grading the EEG than features estimated on the created sub-signals, and performance was further enhanced by adding more sub-grades based on EEG states to the AGS |
Raurale et al. [82] | Quadratic time-frequency distribution with a CNN | EEG data | The proposed EEG HIE-grading system achieved an accuracy of 88.9% and kappa of 0.84 on the development dataset. Accuracy for the large unseen test dataset was 69.5% and kappa of 0.54, which is a significant (p < 0.001) improvement over a state-of-the-art feature-based method with an accuracy of 56.8% and kappa of 0.39 | |
Moghadam et al. [83] | SVM, multilayer feedforward neural network or RNN | EEG data (13,200 5-min EEG epochs) | The optimal solution had a 97% classification accuracy overall, ranging from 81 to 100% across the subjects | |
Matic et al. [84] | Automated algorithm to quantify background EEG abnormalities | Continuous EEG recordings of 1 h | Effective parameterization of continuous EEG data has been achieved resulting in high classification accuracy (89%) to grade background EEG abnormalities | |
Pavel et al. [85] | ML models (random forest and gradient boosting algorithms) using MCC and AUROC | Clinical and EEG parameters at <12 h of birth | Low Apgar, need for ventilation, high lactate, low base excess, absent sleep-wake cycle, low EEG power, and increased EEG discontinuity were associated with seizures. The following predictive models were developed: clinical (MCC 0.368, AUC 0.681), qualitative EEG (MCC 0.467, AUC 0.729), quantitative EEG (MCC 0.473, AUC 0.730), clinical and qualitative EEG (MCC 0.470, AUC 0.721), and clinical and quantitative EEG (MCC 0.513, AUC 0.746). The clinical model by itself performed much worse than the clinical and qualitative-EEG model (MCC 0.470 vs. 0.368, p-value 0.037). With a p-value of 0.012, the clinical model was significantly surpassed by the quantitative-EEG model and clinical model (MCC 0.513 vs. 0.368). Performance for quantitative aEEG was MCC 0.381, AUC 0.696 and clinical and quantitative amplitude EEG was MCC 0.384, AUC 0.720 | |
Sleep stage classification | Ansari et al. [81] | CNN inception block | EEG data | The model significantly outperforms state-of-the-art neonatal quiet sleep detection algorithms, with mean Kappa 0.77 ± 0.01 (with 8-channel EEG) and 0.75 ± 0.01 (with a single bipolar channel EEG) |
Magnetic Resonance Imaging | ||||
Automated segmentation and quantification of the PLIC | Gruber et al. [91] | CNN-based pipeline comprised of slice-selection modules and a multi-view segmentation model | MRI volume data | The proposed method was capable of identifying a specific desired slice from the MRI volume |
Combination of structural and functional networks | Ball et al. [94] | A data-driven, multivariate approach that integrated several imaging modalities | Clinical factors and MRI data | Five independent patterns of neuroanatomical variation that related to clinical factors included age, prematurity, sex, intrauterine complications, and postnatal adversity. It was established that there was a connection between poor cognitive and motor outcomes at two years old and imaging indicators of neuroanatomical abnormalities |
Galdi et al. [95] | Morphometric similarity networks | MRI data, such as density imaging metrics, neurite orientation dispersion, regional volumes, and diffusion tensor metrics | The regression model predicted post-menstrual age at scan with a mean absolute error of 0.70 ± 0.56 weeks; the classification model achieved 92% accuracy | |
Generate reliable and accurate segmentation | Makropoulos et al. [96] | A system for precisely segmenting the developing neonatal brain based on intensity | MRI data | Across a broad range of gestational ages, from 24 weeks gestational age to term-equivalent age, the suggested approach produced extremely accurate results |
Ding et al. [97] | DSC for each tissue type against eight test subjects | MRI data | The best test mean DSC values that were statistically significant were obtained by the dual-modality HyperDense-Net. For all tissue types, T2-weighted image processing performed better by the single-modality LiviaNET than T1-weighted image processing. Both neural networks achieved previously reported performance |
3.2. Neurodevelopmental Outcome
Aim | References | Artificial Intelligence Model | Data-Set Analyzed | Outcome |
---|---|---|---|---|
Detection of neonates with cognitive impairment | Wee et al. [44] | Clustering coefficients of individual structures were computed. SVM and canonical correlation analysis | Diffusion tensor imaging tractography and neurodevelopmental scales | At 24 months of age, the right amygdala’s clustering coefficient was linked to both internalising and externalising behaviours; at 48 months of age, the right inferior frontal cortex and insula’s clustering coefficients were linked to externalising behaviours |
Krishnan et al. [105] | ML using Sparse Reduced Rank Regression | Whole-brain diffusion tractography together with genomewide, SNP-based genotypes and neurodevelopmental scales | SNPs with expected effects such as protein coding and nonsense-mediated decay were found predominantly in introns or regulatory regions of PPARG, where they were significantly overrepresented. The PPARG signaling has a previously unrecognized role in cerebral development | |
Ali et al. [31] | Self-training deep neural network | Brain functional connectome and cognitive assesment data | The proposed model achieved an accuracy of 71.0%, a specificity of 71.5%, a sensitivity of 70.4% and AUROC of 0.75, significantly outperforming transfer learning models through pre-training approaches | |
Detection of neonates at risk of language impairment | Vassar et al. [43] | Multivariate models with leave-one-out cross-validation and exhaustive feature selection | MRI and white matter microstructure assessed on diffusion tensor imaging and neurodevelopmental scales | Based on regional white matter architecture on diffusion tensor imaging, infants at high risk for language impairments were predicted with good accuracy (sensitivity, specificity) for expressive (100%, 90%), receptive language (100%, 90%), and composite (89%, 86%) language |
Valavani et al. [42] | Feature selection and a random forests classifier | MRI data and neurodevelopmental scales | The model achieved balanced accuracy 91%, sensitivity 86%, and specificity 96%. As the values of the radial diffusivity, axial diffusivity, and peak width of skeletonized fractional anisotropy determined from diffusion MRI increased, the likelihood of language delay at two years of corrected age increased as well | |
Detection of neuromotor problems and risk of cerebral palsy | Balta et al. [33] | Tracking software of DeepLabCut using a k-means algorithm | Single commercial videos of six PoIs on the infant’s upper body: left and right shoulders, elbows, and wrists | The results demonstrated that gross motor metrics may be meaningfully estimated and potentially used for early identification of movement disorders |
3.3. Respiratory System
3.4. Ophthalmology
3.5. Gastrointestinal System
3.6. Sepsis
3.7. Patent Ductus Arteriosus
3.8. Dermatology
3.9. Miscellaneous
3.9.1. Vital Signs Monitoring
3.9.2. Neonatal Jaundice
3.10. Mortality
4. Challenges, Limitations, and Future Perspectives of Artificial Intelligence in Neonatology
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Helm, J.M.; Swiergosz, A.M.; Haeberle, H.S.; Karnuta, J.M.; Schaffer, J.L.; Krebs, V.E.; Spitzer, A.I.; Ramkumar, P.N. Machine Learning and Artificial Intelligence: Definitions, Applications, and Future Directions. Curr. Rev. Musculoskelet Med. 2020, 13, 69–76. [Google Scholar] [CrossRef]
- Price, W.N., 2nd; Gerke, S.; Cohen, I.G. Potential Liability for Physicians Using Artificial Intelligence. JAMA 2019, 322, 1765–1766. [Google Scholar] [CrossRef]
- Sujith, A.V.L.N.; Sajja, G.S.; Mahalakshmi, V.; Nuhmani, S.; Prasanalakshmi, B. Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neurosci. Inform. 2022, 2, 100028. [Google Scholar] [CrossRef]
- Esteva, A.; Robicquet, A.; Ramsundar, B.; Kuleshov, V.; DePristo, M.; Chou, K.; Cui, C.; Corrado, G.; Thrun, S.; Dean, J. A guide to deep learning in healthcare. Nat. Med. 2019, 25, 24–29. [Google Scholar] [CrossRef] [PubMed]
- Sarker, I.H. Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions. SN Comput. Sci. 2021, 2, 420. [Google Scholar] [CrossRef] [PubMed]
- Rubinger, L.; Gazendam, A.; Ekhtiari, S.; Bhandari, M. Machine learning and artificial intelligence in research and healthcare. Injury 2023, 54, S69–S73. [Google Scholar] [CrossRef]
- Meskó, B.; Görög, M. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit. Med. 2020, 3, 126. [Google Scholar] [CrossRef]
- Liu, A.; Hahn, J.S.; Heldt, G.P.; Coen, R.W. Detection of neonatal seizures through computerized EEG analysis. Electroencephalogr. Clin. Neurophysiol. 1992, 82, 30–37. [Google Scholar] [CrossRef]
- Gotman, J.; Flanagan, D.; Zhang, J.; Rosenblatt, B. Automatic seizure detection in the newborn: Methods and initial evaluation. Electroencephalogr. Clin. Neurophysiol. 1997, 103, 356–362. [Google Scholar] [CrossRef]
- Ahmed, W.; Veluthandath, A.V.; Rowe, D.J.; Madsen, J.; Clark, H.W.; Postle, A.D.; Wilkinson, J.S.; Murugan, G.S. Prediction of Neonatal Respiratory Distress Biomarker Concentration by Application of Machine Learning to Mid-Infrared Spectra. Sensors 2022, 22, 1744. [Google Scholar] [CrossRef] [PubMed]
- Raimondi, F.; Migliaro, F.; Verdoliva, L.; Gragnaniello, D.; Poggi, G.; Kosova, R.; Sansone, C.; Vallone, G.; Capasso, L. Visual assessment versus computer-assisted gray scale analysis in the ultrasound evaluation of neonatal respiratory status. PLoS ONE 2018, 13, e0202397. [Google Scholar] [CrossRef] [PubMed]
- Dai, D.; Chen, H.; Dong, X.; Chen, J.; Mei, M.; Lu, Y.; Yang, L.; Wu, B.; Cao, Y.; Wang, J.; et al. Bronchopulmonary Dysplasia Predicted by Developing a Machine Learning Model of Genetic and Clinical Information. Front. Genet. 2021, 12, 689071. [Google Scholar] [CrossRef] [PubMed]
- Laughon, M.M.; Langer, J.C.; Bose, C.L.; Smith, P.B.; Ambalavanan, N.; Kennedy, K.A.; Stoll, B.J.; Buchter, S.; Laptook, A.R.; Ehrenkranz, R.A.; et al. Prediction of bronchopulmonary dysplasia by postnatal age in extremely premature infants. Am. J. Respir. Crit. Care Med. 2011, 183, 1715–1722. [Google Scholar] [CrossRef] [PubMed]
- Leigh, R.M.; Pham, A.; Rao, S.S.; Vora, F.M.; Hou, G.; Kent, C.; Rodriguez, A.; Narang, A.; Tan, J.B.C.; Chou, F.S. Machine learning for prediction of bronchopulmonary dysplasia-free survival among very preterm infants. BMC Pediatr. 2022, 22, 542. [Google Scholar] [CrossRef]
- Patel, M.; Sandhu, J.; Chou, F.S. Developing a machine learning-based tool to extend the usability of the NICHD BPD Outcome Estimator to the Asian population. PLoS ONE 2022, 17, e0272709. [Google Scholar] [CrossRef] [PubMed]
- Verder, H.; Heiring, C.; Ramanathan, R.; Scoutaris, N.; Verder, P.; Jessen, T.E.; Hoskuldsson, A.; Bender, L.; Dahl, M.; Eschen, C.; et al. Bronchopulmonary dysplasia predicted at birth by artificial intelligence. Acta Paediatr. 2021, 110, 503–509. [Google Scholar] [CrossRef]
- Xing, W.; He, W.; Li, X.; Chen, J.; Cao, Y.; Zhou, W.; Shen, Q.; Zhang, X.; Ta, D. Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation. Comput. Methods Programs Biomed. 2022, 221, 106869. [Google Scholar] [CrossRef]
- Gomez-Quintana, S.; Schwarz, C.E.; Shelevytsky, I.; Shelevytska, V.; Semenova, O.; Factor, A.; Popovici, E.; Temko, A. A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram. Healthcare 2021, 9, 169. [Google Scholar] [CrossRef]
- Na, J.Y.; Kim, D.; Kwon, A.M.; Jeon, J.Y.; Kim, H.; Kim, C.R.; Lee, H.J.; Lee, J.; Park, H.K. Artificial intelligence model comparison for risk factor analysis of patent ductus arteriosus in nationwide very low birth weight infants cohort. Sci. Rep. 2021, 11, 22353. [Google Scholar] [CrossRef]
- Adam, J.; Rupprecht, S.; Kunstler, E.C.S.; Hoyer, D. Heart rate variability as a marker and predictor of inflammation, nosocomial infection, and sepsis—A systematic review. Auton. Neurosci. 2023, 249, 103116. [Google Scholar] [CrossRef]
- Cabrera-Quiros, L.; Kommers, D.; Wolvers, M.K.; Oosterwijk, L.; Arents, N.; van der Sluijs-Bens, J.; Cottaar, E.J.E.; Andriessen, P.; van Pul, C. Prediction of Late-Onset Sepsis in Preterm Infants Using Monitoring Signals and Machine Learning. Crit. Care Explor. 2021, 3, e0302. [Google Scholar] [CrossRef] [PubMed]
- Ataer-Cansizoglu, E.; Bolon-Canedo, V.; Campbell, J.P.; Bozkurt, A.; Erdogmus, D.; Kalpathy-Cramer, J.; Patel, S.; Jonas, K.; Chan, R.V.; Ostmo, S.; et al. Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the “i-ROP” System and Image Features Associated With Expert Diagnosis. Transl. Vis. Sci. Technol. 2015, 4, 5. [Google Scholar] [CrossRef] [PubMed]
- Barrero-Castillero, A.; Corwin, B.K.; VanderVeen, D.K.; Wang, J.C. Workforce Shortage for Retinopathy of Prematurity Care and Emerging Role of Telehealth and Artificial Intelligence. Pediatr. Clin. N. Am. 2020, 67, 725–733. [Google Scholar] [CrossRef] [PubMed]
- Biten, H.; Redd, T.K.; Moleta, C.; Campbell, J.P.; Ostmo, S.; Jonas, K.; Chan, R.V.P.; Chiang, M.F. Diagnostic Accuracy of Ophthalmoscopy vs. Telemedicine in Examinations for Retinopathy of Prematurity. JAMA Ophthalmol. 2018, 136, 498–504. [Google Scholar] [CrossRef]
- Brown, J.M.; Campbell, J.P.; Beers, A.; Chang, K.; Ostmo, S.; Chan, R.V.P.; Dy, J.; Erdogmus, D.; Ioannidis, S.; Kalpathy-Cramer, J.; et al. Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks. JAMA Ophthalmol. 2018, 136, 803–810. [Google Scholar] [CrossRef] [PubMed]
- Campbell, J.P.; Singh, P.; Redd, T.K.; Brown, J.M.; Shah, P.K.; Subramanian, P.; Rajan, R.; Valikodath, N.; Cole, E.; Ostmo, S.; et al. Applications of Artificial Intelligence for Retinopathy of Prematurity Screening. Pediatrics 2021, 147, e2020016618. [Google Scholar] [CrossRef] [PubMed]
- Chiang, M.F.; Melia, M.; Buffenn, A.N.; Lambert, S.R.; Recchia, F.M.; Simpson, J.L.; Yang, M.B. Detection of clinically significant retinopathy of prematurity using wide-angle digital retinal photography: A report by the American Academy of Ophthalmology. Ophthalmology 2012, 119, 1272–1280. [Google Scholar] [CrossRef] [PubMed]
- Redd, T.K.; Campbell, J.P.; Brown, J.M.; Kim, S.J.; Ostmo, S.; Chan, R.V.P.; Dy, J.; Erdogmus, D.; Ioannidis, S.; Kalpathy-Cramer, J.; et al. Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. Br. J. Ophthalmol. 2018, 103, 580–584. [Google Scholar] [CrossRef] [PubMed]
- Taylor, S.; Brown, J.M.; Gupta, K.; Campbell, J.P.; Ostmo, S.; Chan, R.V.P.; Dy, J.; Erdogmus, D.; Ioannidis, S.; Kim, S.J.; et al. Monitoring Disease Progression With a Quantitative Severity Scale for Retinopathy of Prematurity Using Deep Learning. JAMA Ophthalmol. 2019, 137, 1022–1028. [Google Scholar] [CrossRef]
- Wu, Q.; Hu, Y.; Mo, Z.; Wu, R.; Zhang, X.; Yang, Y.; Liu, B.; Xiao, Y.; Zeng, X.; Lin, Z.; et al. Development and Validation of a Deep Learning Model to Predict the Occurrence and Severity of Retinopathy of Prematurity. JAMA Netw. Open 2022, 5, e2217447. [Google Scholar] [CrossRef]
- Ali, R.; Li, H.; Dillman, J.R.; Altaye, M.; Wang, H.; Parikh, N.A.; He, L. A self-training deep neural network for early prediction of cognitive deficits in very preterm infants using brain functional connectome data. Pediatr. Radiol. 2022, 52, 2227–2240. [Google Scholar] [CrossRef] [PubMed]
- Ambalavanan, N.; Carlo, W.A.; Bobashev, G.; Mathias, E.; Liu, B.; Poole, K.; Fanaroff, A.A.; Stoll, B.J.; Ehrenkranz, R.; Wright, L.L.; et al. Prediction of death for extremely low birth weight neonates. Pediatrics 2005, 116, 1367–1373. [Google Scholar] [CrossRef]
- Balta, D.; Kuo, H.; Wang, J.; Porco, I.G.; Morozova, O.; Schladen, M.M.; Cereatti, A.; Lum, P.S.; Della Croce, U. Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study. Sensors 2022, 22, 7426. [Google Scholar] [CrossRef]
- Do, H.J.; Moon, K.M.; Jin, H.S. Machine Learning Models for Predicting Mortality in 7472 Very Low Birth Weight Infants Using Data from a Nationwide Neonatal Network. Diagnostics 2022, 12, 625. [Google Scholar] [CrossRef] [PubMed]
- Hsu, J.F.; Yang, C.; Lin, C.Y.; Chu, S.M.; Huang, H.R.; Chiang, M.C.; Wang, H.C.; Liao, W.C.; Fu, R.H.; Tsai, M.H. Machine Learning Algorithms to Predict Mortality of Neonates on Mechanical Intubation for Respiratory Failure. Biomedicines 2021, 9, 1377. [Google Scholar] [CrossRef] [PubMed]
- Moreira, A.; Benvenuto, D.; Fox-Good, C.; Alayli, Y.; Evans, M.; Jonsson, B.; Hakansson, S.; Harper, N.; Kim, J.; Norman, M.; et al. Development and Validation of a Mortality Prediction Model in Extremely Low Gestational Age Neonates. Neonatology 2022, 119, 418–427. [Google Scholar] [CrossRef] [PubMed]
- Nascimento, L.F.; Ortega, N.R. Fuzzy linguistic model for evaluating the risk of neonatal death. Rev. Saude Publica 2002, 36, 686–692. [Google Scholar] [CrossRef] [PubMed]
- Podda, M.; Bacciu, D.; Micheli, A.; Bellu, R.; Placidi, G.; Gagliardi, L. A machine learning approach to estimating preterm infants survival: Development of the Preterm Infants Survival Assessment (PISA) predictor. Sci. Rep. 2018, 8, 13743. [Google Scholar] [CrossRef]
- Schadl, K.; Vassar, R.; Cahill-Rowley, K.; Yeom, K.W.; Stevenson, D.K.; Rose, J. Prediction of cognitive and motor development in preterm children using exhaustive feature selection and cross-validation of near-term white matter microstructure. Neuroimage Clin. 2018, 17, 667–679. [Google Scholar] [CrossRef]
- Smyser, C.D.; Dosenbach, N.U.; Smyser, T.A.; Snyder, A.Z.; Rogers, C.E.; Inder, T.E.; Schlaggar, B.L.; Neil, J.J. Prediction of brain maturity in infants using machine-learning algorithms. Neuroimage 2016, 136, 1–9. [Google Scholar] [CrossRef]
- Sripada, K.; Bjuland, K.J.; Solsnes, A.E.; Haberg, A.K.; Grunewaldt, K.H.; Lohaugen, G.C.; Rimol, L.M.; Skranes, J. Trajectories of brain development in school-age children born preterm with very low birth weight. Sci. Rep. 2018, 8, 15553. [Google Scholar] [CrossRef]
- Valavani, E.; Blesa, M.; Galdi, P.; Sullivan, G.; Dean, B.; Cruickshank, H.; Sitko-Rudnicka, M.; Bastin, M.E.; Chin, R.F.M.; MacIntyre, D.J.; et al. Language function following preterm birth: Prediction using machine learning. Pediatr. Res. 2022, 92, 480–489. [Google Scholar] [CrossRef] [PubMed]
- Vassar, R.; Schadl, K.; Cahill-Rowley, K.; Yeom, K.; Stevenson, D.; Rose, J. Neonatal Brain Microstructure and Machine-Learning-Based Prediction of Early Language Development in Children Born Very Preterm. Pediatr. Neurol. 2020, 108, 86–92. [Google Scholar] [CrossRef] [PubMed]
- Wee, C.Y.; Tuan, T.A.; Broekman, B.F.; Ong, M.Y.; Chong, Y.S.; Kwek, K.; Shek, L.P.; Saw, S.M.; Gluckman, P.D.; Fortier, M.V.; et al. Neonatal neural networks predict children behavioral profiles later in life. Hum. Brain. Mapp. 2017, 38, 1362–1373. [Google Scholar] [CrossRef]
- Zimmer, V.A.; Glocker, B.; Hahner, N.; Eixarch, E.; Sanroma, G.; Gratacos, E.; Rueckert, D.; Gonzalez Ballester, M.A.; Piella, G. Learning and combining image neighborhoods using random forests for neonatal brain disease classification. Med. Image Anal. 2017, 42, 189–199. [Google Scholar] [CrossRef]
- Rajpurkar, P.; Chen, E.; Banerjee, O.; Topol, E.J. AI in health and medicine. Nat. Med. 2022, 28, 31–38. [Google Scholar] [CrossRef] [PubMed]
- Adegboro, C.O.; Choudhury, A.; Asan, O.; Kelly, M.M. Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review. Hosp. Pediatr. 2022, 12, 93–110. [Google Scholar] [CrossRef]
- Choudhury, A.; Asan, O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med. Inf. 2020, 8, e18599. [Google Scholar] [CrossRef]
- Choudhury, A.; Renjilian, E.; Asan, O. Use of machine learning in geriatric clinical care for chronic diseases: A systematic literature review. JAMIA Open 2020, 3, 459–471. [Google Scholar] [CrossRef]
- Olive, M.K.; Owens, G.E. Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit. Transl. Pediatr. 2018, 7, 120–128. [Google Scholar] [CrossRef]
- Piccialli, F.; Somma, V.D.; Giampaolo, F.; Cuomo, S.; Fortino, G. A survey on deep learning in medicine: Why, how and when? Inf. Fusion 2021, 66, 111–137. [Google Scholar] [CrossRef]
- Burt, J.R.; Torosdagli, N.; Khosravan, N.; RaviPrakash, H.; Mortazi, A.; Tissavirasingham, F.; Hussein, S.; Bagci, U. Deep learning beyond cats and dogs: Recent advances in diagnosing breast cancer with deep neural networks. Br. J. Radiol. 2018, 91, 20170545. [Google Scholar] [CrossRef] [PubMed]
- Erickson, B.J.; Korfiatis, P.; Kline, T.L.; Akkus, Z.; Philbrick, K.; Weston, A.D. Deep Learning in Radiology: Does One Size Fit All? J. Am. Coll. Radiol. 2018, 15, 521–526. [Google Scholar] [CrossRef] [PubMed]
- Alzubaidi, L.; Zhang, J.; Humaidi, A.J.; Al-Dujaili, A.; Duan, Y.; Al-Shamma, O.; Santamaría, J.; Fadhel, M.A.; Al-Amidie, M.; Farhan, L. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. J. Big Data 2021, 8, 53. [Google Scholar] [CrossRef] [PubMed]
- Ghosh, A.; Sufian, A.; Sultana, F.; Chakrabarti, A.; De, D. Fundamental Concepts of Convolutional Neural Network. In Recent Trends and Advances in Artificial Intelligence and Internet of Things; Intelligent Systems Reference Library; Springer International Publishing: Cham, Switzerland, 2020; pp. 519–567. [Google Scholar]
- LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444. [Google Scholar] [CrossRef] [PubMed]
- Ker, J.; Wang, L.; Rao, J.; Lim, T. Deep Learning Applications in Medical Image Analysis. IEEE Access 2018, 6, 9375–9389. [Google Scholar] [CrossRef]
- Kreimeyer, K.; Foster, M.; Pandey, A.; Arya, N.; Halford, G.; Jones, S.F.; Forshee, R.; Walderhaug, M.; Botsis, T. Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review. J. Biomed. Inf. 2017, 73, 14–29. [Google Scholar] [CrossRef]
- Nadkarni, P.M.; Ohno-Machado, L.; Chapman, W.W. Natural language processing: An introduction. J. Am. Med. Inf. Assoc. 2011, 18, 544–551. [Google Scholar] [CrossRef] [PubMed]
- Brinkmann, B.H.; Bower, M.R.; Stengel, K.A.; Worrell, G.A.; Stead, M. Large-scale electrophysiology: Acquisition, compression, encryption, and storage of big data. J. Neurosci. Methods 2009, 180, 185–192. [Google Scholar] [CrossRef] [PubMed]
- Sheth, R.D.; Hobbs, G.R.; Mullett, M. Neonatal seizures: Incidence, onset, and etiology by gestational age. J. Perinatol. 1999, 19, 40–43. [Google Scholar] [CrossRef]
- Williams, R.P.; Banwell, B.; Berg, R.A.; Dlugos, D.J.; Donnelly, M.; Ichord, R.; Kessler, S.K.; Lavelle, J.; Massey, S.L.; Hewlett, J.; et al. Impact of an ICU EEG monitoring pathway on timeliness of therapeutic intervention and electrographic seizure termination. Epilepsia 2016, 57, 786–795. [Google Scholar] [CrossRef]
- Payne, E.T.; Zhao, X.Y.; Frndova, H.; McBain, K.; Sharma, R.; Hutchison, J.S.; Hahn, C.D. Seizure burden is independently associated with short term outcome in critically ill children. Brain 2014, 137, 1429–1438. [Google Scholar] [CrossRef]
- Chapman, K.E.; Specchio, N.; Shinnar, S.; Holmes, G.L. Seizing control of epileptic activity can improve outcome. Epilepsia 2015, 56, 1482–1485. [Google Scholar] [CrossRef]
- Murray, D.M.; Boylan, G.B.; Ali, I.; Ryan, C.A.; Murphy, B.P.; Connolly, S. Defining the gap between electrographic seizure burden, clinical expression and staff recognition of neonatal seizures. Arch. Dis. Child Fetal. Neonatal. Ed. 2008, 93, F187–F191. [Google Scholar] [CrossRef] [PubMed]
- Shellhaas, R.A.; Clancy, R.R. Characterization of neonatal seizures by conventional EEG and single-channel EEG. Clin. Neurophysiol. 2007, 118, 2156–2161. [Google Scholar] [CrossRef] [PubMed]
- Scher, M.S.; Alvin, J.; Gaus, L.; Minnigh, B.; Painter, M.J. Uncoupling of EEG-clinical neonatal seizures after antiepileptic drug use. Pediatr. Neurol. 2003, 28, 277–280. [Google Scholar] [CrossRef]
- McCoy, B.; Hahn, C.D. Continuous EEG monitoring in the neonatal intensive care unit. J. Clin. Neurophysiol. 2013, 30, 106–114. [Google Scholar] [CrossRef] [PubMed]
- Shellhaas, R.A. Continuous long-term electroencephalography: The gold standard for neonatal seizure diagnosis. Semin. Fetal. Neonatal. Med. 2015, 20, 149–153. [Google Scholar] [CrossRef]
- Shellhaas, R.A.; Chang, T.; Tsuchida, T.; Scher, M.S.; Riviello, J.J.; Abend, N.S.; Nguyen, S.; Wusthoff, C.J.; Clancy, R.R. The American Clinical Neurophysiology Society’s Guideline on Continuous Electroencephalography Monitoring in Neonates. J. Clin. Neurophysiol. 2011, 28, 611–617. [Google Scholar] [CrossRef]
- de Vries, L.S.; Toet, M.C. Amplitude integrated electroencephalography in the full-term newborn. Clin. Perinatol. 2006, 33, 619–632. [Google Scholar] [CrossRef]
- de Vries, L.S.; Hellstrom-Westas, L. Role of cerebral function monitoring in the newborn. Arch. Dis. Child. Fetal. Neonatal. Ed. 2005, 90, F201–F207. [Google Scholar] [CrossRef] [PubMed]
- Rakshasbhuvankar, A.; Rao, S.; Palumbo, L.; Ghosh, S.; Nagarajan, L. Amplitude Integrated Electroencephalography Compared With Conventional Video EEG for Neonatal Seizure Detection: A Diagnostic Accuracy Study. J. Child. Neurol. 2017, 32, 815–822. [Google Scholar] [CrossRef]
- Appendino, J.P.; McNamara, P.J.; Keyzers, M.; Stephens, D.; Hahn, C.D. The impact of amplitude-integrated electroencephalography on NICU practice. Can. J. Neurol. Sci. 2012, 39, 355–360. [Google Scholar] [CrossRef] [PubMed]
- Temko, A.; Lightbody, G. Detecting Neonatal Seizures With Computer Algorithms. J. Clin. Neurophysiol. 2016, 33, 394–402. [Google Scholar] [CrossRef]
- O’Shea, A.; Lightbody, G.; Boylan, G.; Temko, A. Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture. Neural. Netw. 2020, 123, 12–25. [Google Scholar] [CrossRef]
- O’Shea, A.; Ahmed, R.; Lightbody, G.; Pavlidis, E.; Lloyd, R.; Pisani, F.; Marnane, W.; Mathieson, S.; Boylan, G.; Temko, A. Deep Learning for EEG Seizure Detection in Preterm Infants. Int. J. Neural. Syst. 2021, 31, 2150008. [Google Scholar] [CrossRef] [PubMed]
- Pavel, A.M.; Rennie, J.M.; de Vries, L.S.; Blennow, M.; Foran, A.; Shah, D.K.; Pressler, R.M.; Kapellou, O.; Dempsey, E.M.; Mathieson, S.R.; et al. A machine-learning algorithm for neonatal seizure recognition: A multicentre, randomised, controlled trial. Lancet Child Adolesc. Health 2020, 4, 740–749. [Google Scholar] [CrossRef]
- Stevenson, N.J.; Korotchikova, I.; Temko, A.; Lightbody, G.; Marnane, W.P.; Boylan, G.B. An automated system for grading EEG abnormality in term neonates with hypoxic-ischaemic encephalopathy. Ann. Biomed. Eng. 2013, 41, 775–785. [Google Scholar] [CrossRef] [PubMed]
- Mathieson, S.R.; Stevenson, N.J.; Low, E.; Marnane, W.P.; Rennie, J.M.; Temko, A.; Lightbody, G.; Boylan, G.B. Validation of an automated seizure detection algorithm for term neonates. Clin. Neurophysiol. 2016, 127, 156–168. [Google Scholar] [CrossRef]
- Ansari, A.H.; Pillay, K.; Dereymaeker, A.; Jansen, K.; Van Huffel, S.; Naulaers, G.; De Vos, M. A Deep Shared Multi-Scale Inception Network Enables Accurate Neonatal Quiet Sleep Detection With Limited EEG Channels. IEEE J. Biomed. Health Inf. 2022, 26, 1023–1033. [Google Scholar] [CrossRef]
- Raurale, S.A.; Boylan, G.B.; Mathieson, S.R.; Marnane, W.P.; Lightbody, G.; O’Toole, J.M. Grading hypoxic-ischemic encephalopathy in neonatal EEG with convolutional neural networks and quadratic time-frequency distributions. J. Neural. Eng. 2021, 18, 046007. [Google Scholar] [CrossRef]
- Moghadam, S.M.; Pinchefsky, E.; Tse, I.; Marchi, V.; Kohonen, J.; Kauppila, M.; Airaksinen, M.; Tapani, K.; Nevalainen, P.; Hahn, C.; et al. Building an Open Source Classifier for the Neonatal EEG Background: A Systematic Feature-Based Approach From Expert Scoring to Clinical Visualization. Front. Hum. Neurosci. 2021, 15, 675154. [Google Scholar] [CrossRef]
- Matic, V.; Cherian, P.J.; Koolen, N.; Naulaers, G.; Swarte, R.M.; Govaert, P.; Van Huffel, S.; De Vos, M. Holistic approach for automated background EEG assessment in asphyxiated full-term infants. J. Neural. Eng. 2014, 11, 066007. [Google Scholar] [CrossRef] [PubMed]
- Pavel, A.M.; O’Toole, J.M.; Proietti, J.; Livingstone, V.; Mitra, S.; Marnane, W.P.; Finder, M.; Dempsey, E.M.; Murray, D.M.; Boylan, G.B.; et al. Machine learning for the early prediction of infants with electrographic seizures in neonatal hypoxic-ischemic encephalopathy. Epilepsia 2023, 64, 456–468. [Google Scholar] [CrossRef] [PubMed]
- Serag, A.; Blesa, M.; Moore, E.J.; Pataky, R.; Sparrow, S.A.; Wilkinson, A.G.; Macnaught, G.; Semple, S.I.; Boardman, J.P. Accurate Learning with Few Atlases (ALFA): An algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods. Sci. Rep. 2016, 6, 23470. [Google Scholar] [CrossRef]
- Blesa, M.; Galdi, P.; Cox, S.R.; Sullivan, G.; Stoye, D.Q.; Lamb, G.J.; Quigley, A.J.; Thrippleton, M.J.; Escudero, J.; Bastin, M.E.; et al. Hierarchical Complexity of the Macro-Scale Neonatal Brain. Cereb. Cortex. 2021, 31, 2071–2084. [Google Scholar] [CrossRef]
- De Vries, L.S.; Groenendaal, F.; van Haastert, I.C.; Eken, P.; Rademaker, K.J.; Meiners, L.C. Asymmetrical myelination of the posterior limb of the internal capsule in infants with periventricular haemorrhagic infarction: An early predictor of hemiplegia. Neuropediatrics 1999, 30, 314–319. [Google Scholar] [CrossRef]
- Odding, E.; Roebroeck, M.E.; Stam, H.J. The epidemiology of cerebral palsy: Incidence, impairments and risk factors. Disabil. Rehabil. 2006, 28, 183–191. [Google Scholar] [CrossRef] [PubMed]
- Drougia, A.; Giapros, V.; Krallis, N.; Theocharis, P.; Nikaki, A.; Tzoufi, M.; Andronikou, S. Incidence and risk factors for cerebral palsy in infants with perinatal problems: A 15-year review. Early Hum. Dev. 2007, 83, 541–547. [Google Scholar] [CrossRef]
- Gruber, N.; Galijasevic, M.; Regodic, M.; Grams, A.E.; Siedentopf, C.; Steiger, R.; Hammerl, M.; Haltmeier, M.; Gizewski, E.R.; Janjic, T. A deep learning pipeline for the automated segmentation of posterior limb of internal capsule in preterm neonates. Artif. Intell. Med. 2022, 132, 102384. [Google Scholar] [CrossRef]
- Dean, B.; Ginnell, L.; Boardman, J.P.; Fletcher-Watson, S. Social cognition following preterm birth: A systematic review. Neurosci. Biobehav. Rev. 2021, 124, 151–167. [Google Scholar] [CrossRef] [PubMed]
- Batalle, D.; Edwards, A.D.; O’Muircheartaigh, J. Annual Research Review: Not just a small adult brain: Understanding later neurodevelopment through imaging the neonatal brain. J. Child Psychol. Psychiatry 2018, 59, 350–371. [Google Scholar] [CrossRef] [PubMed]
- Ball, G.; Aljabar, P.; Nongena, P.; Kennea, N.; Gonzalez-Cinca, N.; Falconer, S.; Chew, A.T.M.; Harper, N.; Wurie, J.; Rutherford, M.A.; et al. Multimodal image analysis of clinical influences on preterm brain development. Ann. Neurol. 2017, 82, 233–246. [Google Scholar] [CrossRef]
- Galdi, P.; Blesa, M.; Stoye, D.Q.; Sullivan, G.; Lamb, G.J.; Quigley, A.J.; Thrippleton, M.J.; Bastin, M.E.; Boardman, J.P. Neonatal morphometric similarity mapping for predicting brain age and characterizing neuroanatomic variation associated with preterm birth. Neuroimage Clin. 2020, 25, 102195. [Google Scholar] [CrossRef] [PubMed]
- Makropoulos, A.; Gousias, I.S.; Ledig, C.; Aljabar, P.; Serag, A.; Hajnal, J.V.; Edwards, A.D.; Counsell, S.J.; Rueckert, D. Automatic whole brain MRI segmentation of the developing neonatal brain. IEEE Trans. Med. Imaging 2014, 33, 1818–1831. [Google Scholar] [CrossRef] [PubMed]
- Ding, Y.; Acosta, R.; Enguix, V.; Suffren, S.; Ortmann, J.; Luck, D.; Dolz, J.; Lodygensky, G.A. Using Deep Convolutional Neural Networks for Neonatal Brain Image Segmentation. Front. Neurosci. 2020, 14, 207. [Google Scholar] [CrossRef] [PubMed]
- Shang, J.; Fisher, P.; Bauml, J.G.; Daamen, M.; Baumann, N.; Zimmer, C.; Bartmann, P.; Boecker, H.; Wolke, D.; Sorg, C.; et al. A machine learning investigation of volumetric and functional MRI abnormalities in adults born preterm. Hum. Brain Mapp. 2019, 40, 4239–4252. [Google Scholar] [CrossRef] [PubMed]
- Chiarelli, A.M.; Sestieri, C.; Navarra, R.; Wise, R.G.; Caulo, M. Distinct effects of prematurity on MRI metrics of brain functional connectivity, activity, and structure: Univariate and multivariate analyses. Hum. Brain Mapp. 2021, 42, 3593–3607. [Google Scholar] [CrossRef] [PubMed]
- Ball, G.; Aljabar, P.; Arichi, T.; Tusor, N.; Cox, D.; Merchant, N.; Nongena, P.; Hajnal, J.V.; Edwards, A.D.; Counsell, S.J. Machine-learning to characterise neonatal functional connectivity in the preterm brain. Neuroimage 2016, 124, 267–275. [Google Scholar] [CrossRef]
- Song, Z.; Awate, S.P.; Licht, D.J.; Gee, J.C. Clinical neonatal brain MRI segmentation using adaptive nonparametric data models and intensity-based Markov priors. Med. Image Comput. Comput. Assist. Interv. 2007, 10, 883–890. [Google Scholar] [CrossRef]
- Keunen, K.; Counsell, S.J.; Benders, M. The emergence of functional architecture during early brain development. Neuroimage 2017, 160, 2–14. [Google Scholar] [CrossRef]
- Gao, W.; Lin, W.; Grewen, K.; Gilmore, J.H. Functional Connectivity of the Infant Human Brain: Plastic and Modifiable. Neuroscientist 2017, 23, 169–184. [Google Scholar] [CrossRef]
- Li, Y.; Zhang, X.; Nie, J.; Zhang, G.; Fang, R.; Xu, X.; Wu, Z.; Hu, D.; Wang, L.; Zhang, H.; et al. Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction. IEEE Trans. Med. Imaging 2022, 41, 2764–2776. [Google Scholar] [CrossRef] [PubMed]
- Krishnan, M.L.; Wang, Z.; Aljabar, P.; Ball, G.; Mirza, G.; Saxena, A.; Counsell, S.J.; Hajnal, J.V.; Montana, G.; Edwards, A.D. Machine learning shows association between genetic variability in PPARG and cerebral connectivity in preterm infants. Proc. Natl. Acad. Sci. USA 2017, 114, 13744–13749. [Google Scholar] [CrossRef] [PubMed]
- Mueller, M.; Wagner, C.L.; Annibale, D.J.; Hulsey, T.C.; Knapp, R.G.; Almeida, J.S. Predicting extubation outcome in preterm newborns: A comparison of neural networks with clinical expertise and statistical modeling. Pediatr. Res. 2004, 56, 11–18. [Google Scholar] [CrossRef] [PubMed]
- Precup, D.; Robles-Rubio, C.A.; Brown, K.A.; Kanbar, L.; Kaczmarek, J.; Chawla, S.; Sant’Anna, G.M.; Kearney, R.E. Prediction of extubation readiness in extreme preterm infants based on measures of cardiorespiratory variability. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2012, 2012, 5630–5633. [Google Scholar] [CrossRef]
- Mikhno, A.; Ennett, C.M. Prediction of extubation failure for neonates with respiratory distress syndrome using the MIMIC-II clinical database. In Proceedings of the 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, USA, 28 August–1 September 2012. [Google Scholar] [CrossRef]
- Eichenwald, E.C.; Committee on Fetus and Newborn; Watterberg, K.L.; Aucott, S.; Benitz, W.E.; Cummings, J.J.; Goldsmith, J.; Poindexter, B.B.; Puopolo, K.; Stewart, D.L.; et al. Apnea of Prematurity. Pediatrics 2016, 137, e20153757. [Google Scholar] [CrossRef]
- Amin, S.B.; Burnell, E. Monitoring apnea of prematurity: Validity of nursing documentation and bedside cardiorespiratory monitor. Am. J. Perinatol. 2013, 30, 643–648. [Google Scholar] [CrossRef] [PubMed]
- Varisco, G.; Peng, Z.; Kommers, D.; Zhan, Z.; Cottaar, W.; Andriessen, P.; Long, X.; van Pul, C. Central apnea detection in premature infants using machine learning. Comput Methods Programs Biomed. 2022, 226, 107155. [Google Scholar] [CrossRef]
- Son, J.; Kim, D.; Na, J.Y.; Jung, D.; Ahn, J.H.; Kim, T.H.; Park, H.K. Development of artificial neural networks for early prediction of intestinal perforation in preterm infants. Sci. Rep. 2022, 12, 12112. [Google Scholar] [CrossRef]
- Greenbury, S.F.; Ougham, K.; Wu, J.; Battersby, C.; Gale, C.; Modi, N.; Angelini, E.D. Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning. Sci. Rep. 2021, 11, 7178. [Google Scholar] [CrossRef]
- Han, J.H.; Yoon, S.J.; Lee, H.S.; Park, G.; Lim, J.; Shin, J.E.; Eun, H.S.; Park, M.S.; Lee, S.M. Application of Machine Learning Approaches to Predict Postnatal Growth Failure in Very Low Birth Weight Infants. Yonsei Med. J. 2022, 63, 640–647. [Google Scholar] [CrossRef]
- Shane, A.L.; Sanchez, P.J.; Stoll, B.J. Neonatal sepsis. Lancet 2017, 390, 1770–1780. [Google Scholar] [CrossRef] [PubMed]
- El-Khuffash, A.; Bussmann, N.; Breatnach, C.R.; Smith, A.; Tully, E.; Griffin, J.; McCallion, N.; Corcoran, J.D.; Fernandez, E.; Looi, C.; et al. A Pilot Randomized Controlled Trial of Early Targeted Patent Ductus Arteriosus Treatment Using a Risk Based Severity Score (The PDA RCT). J. Pediatr. 2021, 229, 127–133. [Google Scholar] [CrossRef] [PubMed]
- Krowchuk, D.P.; Frieden, I.J.; Mancini, A.J.; Darrow, D.H.; Blei, F.; Greene, A.K.; Annam, A.; Baker, C.N.; Frommelt, P.C.; Hodak, A.; et al. Clinical Practice Guideline for the Management of Infantile Hemangiomas. Pediatrics 2019, 143, e20183475. [Google Scholar] [CrossRef] [PubMed]
- Zhang, A.J.; Lindberg, N.; Chamlin, S.L.; Haggstrom, A.N.; Mancini, A.J.; Siegel, D.H.; Drolet, B.A. Development of an artificial intelligence algorithm for the diagnosis of infantile hemangiomas. Pediatr. Dermatol. 2022, 39, 934–936. [Google Scholar] [CrossRef] [PubMed]
- Drucker, A.M.; Wang, A.R.; Li, W.-Q.; Sevetson, E.; Block, J.K.; Qureshi, A.A. The Burden of Atopic Dermatitis: Summary of a Report for the National Eczema Association. J. Investig. Dermatol. 2017, 137, 26–30. [Google Scholar] [CrossRef] [PubMed]
- Guimarães, P.; Batista, A.; Zieger, M.; Kaatz, M.; Koenig, K. Artificial Intelligence in Multiphoton Tomography: Atopic Dermatitis Diagnosis. Sci. Rep. 2020, 10, 7968. [Google Scholar] [CrossRef] [PubMed]
- De Guzman, L.C.; Maglaque, R.P.C.; Torres, V.M.B.; Zapido, S.P.A.; Cordel, M.O. Design and Evaluation of a Multi-model, Multi-level Artificial Neural Network for Eczema Skin Lesion Detection. In Proceedings of the 2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS), Kota Kinabalu, Malaysia, 2–4 December 2015; pp. 42–47. [Google Scholar]
- Gustafson, E.; Pacheco, J.; Wehbe, F.; Silverberg, J.; Thompson, W. A Machine Learning Algorithm for Identifying Atopic Dermatitis in Adults from Electronic Health Records. In Proceedings of the 2017 IEEE International Conference on Healthcare Informatics (ICHI), Park City, UT, USA, 23–26 August 2017; pp. 83–90. [Google Scholar]
- Han, S.S.; Park, I.; Eun Chang, S.; Lim, W.; Kim, M.S.; Park, G.H.; Chae, J.B.; Huh, C.H.; Na, J.-I. Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders. J. Investig. Dermatol. 2020, 140, 1753–1761. [Google Scholar] [CrossRef]
- Koller, T.; Navarini, A.; vor der Brück, T.; Pouly, M.; Schnürle, S. On using Support Vector Machines for the Detection and Quantification of Hand Eczema. In Proceedings of the 9th International Conference on Agents and Artificial Intelligence, Porto, Portugal, 24–26 February 2017; pp. 75–84. [Google Scholar]
- Tsien, C.L.; Kohane, I.S.; McIntosh, N. Multiple signal integration by decision tree induction to detect artifacts in the neonatal intensive care unit. Artif. Intell. Med. 2000, 19, 189–202. [Google Scholar] [CrossRef]
- Saria, S.; Rajani, A.K.; Gould, J.; Koller, D.; Penn, A.A. Integration of early physiological responses predicts later illness severity in preterm infants. Sci. Transl. Med. 2010, 2, 48ra65. [Google Scholar] [CrossRef]
- Lyra, S.; Rixen, J.; Heimann, K.; Karthik, S.; Joseph, J.; Jayaraman, K.; Orlikowsky, T.; Sivaprakasam, M.; Leonhardt, S.; Hoog Antink, C. Camera fusion for real-time temperature monitoring of neonates using deep learning. Med. Biol. Eng. Comput 2022, 60, 1787–1800. [Google Scholar] [CrossRef]
- Althnian, A.; Almanea, N.; Aloboud, N. Neonatal Jaundice Diagnosis Using a Smartphone Camera Based on Eye, Skin, and Fused Features with Transfer Learning. Sensors 2021, 21, 7038. [Google Scholar] [CrossRef]
- Guedalia, J.; Farkash, R.; Wasserteil, N.; Kasirer, Y.; Rottenstreich, M.; Unger, R.; Grisaru Granovsky, S. Primary risk stratification for neonatal jaundice among term neonates using machine learning algorithm. Early Hum. Dev. 2022, 165, 105538. [Google Scholar] [CrossRef] [PubMed]
- Pearlman, S.A. Advancements in neonatology through quality improvement. J. Perinatol. 2022, 42, 1277–1282. [Google Scholar] [CrossRef] [PubMed]
- Mangold, C.; Zoretic, S.; Thallapureddy, K.; Moreira, A.; Chorath, K.; Moreira, A. Machine Learning Models for Predicting Neonatal Mortality: A Systematic Review. Neonatology 2021, 118, 394–405. [Google Scholar] [CrossRef]
- Mercurio, M.R.; Cummings, C.L. Critical decision-making in neonatology and pediatrics: The I-P-O framework. J. Perinatol. 2021, 41, 173–178. [Google Scholar] [CrossRef] [PubMed]
- Katznelson, G.; Gerke, S. The need for health AI ethics in medical school education. Adv. Health. Sci. Educ. Theory Pract. 2021, 26, 1447–1458. [Google Scholar] [CrossRef]
- Lin, M.; Vitcov, G.G.; Cummings, C.L. Moral equivalence theory in neonatology. Semin. Perinatol. 2022, 46, 151525. [Google Scholar] [CrossRef]
Aim | References | Artificial Intelligence Method | Data-Set Analyzed | Outcome |
---|---|---|---|---|
RDS severity | Ahmed et al. [10] | Attenuated total reflectance Fourier transform infrared spectroscopy combined with ML, performing callibration of principal component and partial least squares regression model | Two RDS biomarkers, lecithin and sphingomyelin (L/S ratio) | A three-factor model of second derivative spectra best predicted L/S ratios across the full range (R2: 0.967; MSE: 0.014). The L/S ratios from 1.0 to 3.4 were predicted with a prediction interval of +0.29, −0.37 when using a second derivative spectra model and had a mean prediction interval of +0.26, −0.34 around the L/S 2.2 region |
Raimondi et al. [11] | SVM regressor | Lung ultrasonography using grayscale analysis supported by both visual and computer aids | Visual assessment correlated significantly with respiratory indexes with a strong interobserver agreement. The use of regions of interest in the grayscale analysis of lung ultrasonography scans revealed a strong connection with oxygenation indexes | |
Prediction of BPD | Verder et al. [16] | SVM | Clinical and laboratory data | An algorithm combining birth weight, gestational age, and the sectral analysis of the gastric aspirates resulted to a sensitivity of 88% and a specificity of 91% for early diagnosis of BPD |
Dai et al. [12] | Predictive models evaluated using AUROC | Clinical and genetic features | The predictive model for BPD, which combined the BPD rsik score and basic clinical risk factors, showed better discrimination than the model that was only based on basic clinical features (AUROC, 0.915 vs. AUROC, 0.814, p = 0.013, respectively). The severe BPD predictive model had AUROC, 0.907 vs. AUROC, 0.826; p = 0.016 | |
Leigh et al. [14] | A final ensemble model using logistic regression and the AUROC | Perinatal factors and early postnatal respiratory support | The performance of the model showed AUROC 0.921 and 0.899 for the training and the validation datasets, respectively | |
Xing et al. [17] | XSEG-Net model combining digital image processing and human-computer interaction | Chest X-ray images | During the XSEG-Net network’s training, the dice and cross-entropy loss values were 0.9794 and 0.0146, respectively. The deep CNN model based on VGGNet had the promising prediction performance, with the accuracy, precision, sensitivity, and specificity reaching 95.58%, 95.61%, 95.67%, and 96.98%, respectively | |
Laughon et al. [13] | Models using a C statistic and AUROC | Gestational age, birth weight, race, ethnicity, sex, respiratory support, and FiO2 | Prediction improved with advancing postnatal age, increasing from a C statistic of 0.793 on Day 1 to a maximum of 0.854 on Day 28 | |
Patel et al. [15] | Random forest algorithm with AUROC | Three racial/ethnic options | Model had AUROC of 0.934, 0.850, and 0.757 for respiratory outcomes at post-menstrual age 36, 37, and 40 weeks, respectively. An interrelationship among racial/ethnic groups and the feasibility of extending the use of the Estimator to the Asian population was shown | |
Extubation readiness | Mueller et al. [106] | A ML approach using ANNs, multivariate logistic regression and the AUROC | 51 variables | The optimal ANN model used 13 parameters and achieved an AUROC of 0.87, comparing favorably with multivariate logistic regression. It compared well with the clinician’s expertise |
Precup et al. [107] | ML method of SVM | Measures of cardiorespiratory variability | The predictor correctly identified infants who would not survive extubation, according to the results | |
Mikhno et al. [108] | ML approach | Clinical and laboratory factors | Algorithm performance had AUROC of 0.871, sensitivity 70.1%, and specificity 90% | |
Automated detection of apneas | Varisco et al. [111] | Optimized algorithm for automated detection using logistic regression and the AUROC | 47 characteristics were taken out of the oxygen saturation and ECG signals | The apnea detection model returned the highest mean AUROC, both using leave-one-patient-out and 10-fold cross-validation (mean AUROC of 0.88 and 0.90, respectively) |
Aim | References | Artificial Intelligence Model | Data-Set Analyzed | Outcome |
---|---|---|---|---|
Automated diagnosis of ROP | Ataer-Cansizoglu et al. [22] | Computer-based image analysis system (i-ROP) | Retina image | When compared to the reference standard, the i-ROP system classified preplus and plus illness with 95% accuracy. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%) |
Redd et al. [28] | A DL system (i-ROP plus score) on a 1–9 scale | Retina image | The AUROC of 0.960 was found for the i-ROP severity score in identifying type 1 ROP. Establishing a threshold i-ROP score of 3 conferred 94% sensitivity, 79% specificity, 13% positive predictive value and 99.7% negative predictive value for type 1 ROP. The i-ROP DL vascular severity score and expert rank ordering of overall ROP severity revealed a strong correlation (r = 0.93; p < 0.0001) | |
Wu et al. [30] | Two models, OC-Net and SE-Net of ROP. AUROC, accuracy, sensitivity, and specificity | Retina image | AUROC, accuracy, sensitivity, and specificity were 0.90, 52.8%, 100%, and 37.8%, respectively, for OC-Net and 0.87, 68.0%, 100%, and 46.6%, respectively, for SE-Net. In external validation, the AUROC, accuracy, sensitivity, and specificity were 0.94, 33.3%, 100%, and 7.5%, respectively, for OC-Net, and 0.88, 56.0%, 100%, and 35.3% for SE-Net, respectively | |
Biten et al. [24] | Telemedicine diagnoses of all 3 image readers | Retina image | Ophthalmoscopy and telemedicine each had similar sensitivity for zone I disease (78% vs. 78%), plus disease (74% vs. 79%), and type 2 ROP (stage 3, zone I, or plus disease: 86% vs. 79%), but ophthalmoscopy was slightly more sensitive in identifying stage 3 disease (85% vs. 73%; p = 0.004) | |
Brown et al. [25] | Deep CNN algorithm based on deep learning. Receiver operating characteristic analysis was performed | Retina image | The diagnosis of plus disease (as opposed to pre-plus disease or normal) had an average AUROC of 0.98, whereas the diagnosis of normal (as opposed to pre-plus disease or normal) was 0.94. The method achieved 93% sensitivity and 94% specificity for + illness detection. The sensitivity and specificity for identifying pre-plus illness or worse were 100% and 94%, respectively | |
Taylor et al. [29] | An algorithm assessing plus illness and its usefulness for impartially tracking the advancement of ROP | Retina image | The median severity scores for each category were 1.1 (no ROP), 1.5 (mild ROP), 4.6 (type 2 and pre-plus), and 7.5 (treatment-requiring ROP) (p <0.001) | |
Campbell et al. [26] | AI-based quantitative severity scale for ROP and AUROC | Retina image | The AUROC for detection of treatment-requiring retinopathy of prematurity was 0.98, with 100% sensitivity and 78% specificity |
Aim | References | Artificial Intelligence Model | Data-Set Analyzed | Outcome |
---|---|---|---|---|
Gastrointestinal System | ||||
Prediction of spontaneous intestinal perforation | Son et al. [112] | ANNs and AUROC | Clinical data | The ANN models showed AUROC of 0.8832 for predicting intestinal perforation associated with necrotizing enterocolitis and 0.8797 for spontaneous perforation |
Prediction of postnatal growth failure | Han et al. [114] | ML models were built using four different techniques XGB, random forest, SVM, and CNN to compare against the multiple logistic regression model | Clinical data | When compared with multiple logistic regression, XGB showed a significantly higher AUROC (p = 0.03) for Day 7, which was the primary performance metric. Using optimal cut-off points, for Day 7, XGB showed better performances in terms of AUROC (0.74), accuracy (0.68) |
Sepsis | ||||
Prediction of EOS | Adam et al. [20] | ML in form of a random forest classifier | Risk factors, clinical signs and biomarkers | The full model achieved an area under the receiver operating characteristic curve (AUROC) of 83.41% and an area under the precision recall curve 28.42%. The predictive performance of the model with risk factors alone was comparable with random |
Prediction of LOS | Cabrera-Quiros et al. [21] | Three popular ML techniques (naive Bayes, closest mean classifier, and logistic regression) | ECG and respiration data (heart rate variability, respiration, body motion) | Using a combination of all features, classification of LOS and C showed a mean accuracy of 0.79 ± 0.12 and mean precision rate of 0.82 ± 0.18 3 h before the onset of sepsis |
Patent ductus arteriosus | ||||
Detection of PDA | Na et al. [19] | Algorithms including random forest, decision tree-based theory, L-GBM, low-bias model, feedforward ANN, SVM, using multiple logistic regression | Database of risk factors | L-GBM achieved the highest accuracy at predicting PDA (0.77), AUROC (0.82) and specificity (0.84), and logistic regression performed best with sensitivity (0.85). The random forest model achieved the best accuracy (0.85), AUROC (0.82) and sensitivity (0.97) in determining PDA therapy |
Gomez-Quintana et al. [18] | Clinical decision support tool based on ML | Heart sounds | The developed system reached an AUROC of 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort |
Aim | References | Artificial Intelligence Model | Data-Set Analyzed | Outcome |
---|---|---|---|---|
Infantile hemangiomas | Zhang et al. [118] | Artificial intelligence algorithm | Clinical images | The algorithm achieved a 91.7% overall accuracy in the diagnosis of facial infantile hemangiomas |
Atopic dermatitis | Guimaraes et al. [120] | CNN | Images combining both morphological and metabolic information | The algorithm correctly diagnosed atopic dermatitis in 97.0 ± 0.2% of all images presenting living cells. For diagnosis sensitivity was 0.966 ± 0.003 and specificity 0.977 ± 0.003 |
De Guzman et al. [121] | A multi-model, multi-level system using the ANN architecture | When evaluating eczema against non-eczema instances, the system’s average confidence level was 68.37%, compared to 63.01% for the single level, or single model system | ||
Gustafson et al. [122] | ML-based phenotype algorithm, using the electronic health record, combined in a lasso logistic regression | Coded information extracted from encounter notes | The algorithm achieved high positive predictive value and sensitivity. These results demonstrate the utility of natural language processing and ML for electronic health record-based phenotyping | |
Eczema | Han et al. [123] | Artificial intelligence algorithm | Images of 174 disorders | The AUROC for malignancy detection were 0.928 ± 0.002 and 0.937 ± 0.004. The AUROC of primary treatment suggestion were 0.828 ± 0.012, 0.885 ± 0.006, 0.885 ± 0.006, and 0.918 ± 0.006 for steroids, antibiotics, antivirals, and antifungals, respectively. With the assistance of our algorithm, the sensitivity and specificity of clinicians for malignancy prediction were improved by 12.1% (p <0.0001) and 1.1% (p < 0.0001), respectively |
Koller et al. [124] | An automatic image processing method for hand eczema segmentation based on SVM | Several experiments with different feature sets | The system achieved an F1 score of 58.6% for front sides of hands and 43.8% for back sides, which outperforms methods that were tested on the gold standard data set |
Aim | References | Artificial Intelligence Model | Data-Set Analyzed | Outcome |
---|---|---|---|---|
Vital Signs Monitoring | ||||
Detect artifacts | Tsien et al. [125] | Decision tree induction | Multiple physiologic data signals | Finding artefacts was possible by the integration of many signals by using a classification system on sets of values obtained from physiologic data streams |
Predict overall mortality | Saria et al. [126] | Prediction algorithm (PhysiScore) based on a physiological assessment score for preterm newborns | Apgar score and standard signals recorded noninvasively on admission | PhysiScore provided higher accuracy prediction of overall morbidity (86% sensitive at 96% specificity) than other neonatal scoring systems. PhysiScore was particularly accurate at identifying infants with high morbidity related to specific complications (infection: 90% to 100%; cardiopulmonary: 96% to 100%) |
Temperature detection | Lyra et al. [127] | A combination of DL–based algorithms and camera modalities | Thermographic recordings | The keypoint detector’s validation revealed a mean average precision of 0.82. The evaluation of the temperature extraction revealed a mean absolute error of 0.55 °C |
Neonatal Jaundice | ||||
Detection of jaundice | Althnian et al. [128] | DL approach | Eye, skin, and fused images | Traditional models outperformed DL models with eyes and fused features, but DL model did better with skin photos |
Guedalia et al. [129] | ML using a combined data analysis approach with AUROC | Clinical data without serum bilirubin evaluation | The ML diagnostic ability to evaluate the risk for neonatal jaundice was 0.748 (AUROC). Important factors were maternal blood type, maternal age, gestational age, estimated birth weight, parity, full blood count, and maternal blood pressure |
Aim | References | Artificial Intelligence Model | Data-Set Analyzed | Outcome |
---|---|---|---|---|
Prediction of mortality | Podda et al. [38] | ML methods including ANN, using logistic regression models | Twelve easily collected perinatal variables | ANN had a slightly better discrimination than logistic regression. Using a cutoff of death probability of 0.5, logistic regression misclassified 1.2 percent more than ANN |
Ambalavanan et al. [32] | Logistic regression and neural network models | Twenty-eight routinely collected variables were selected and multiple scenarios were created | The prediction was best with scenario C (AUROC: 0.85 for regression; 0.84 for neural networks), compared with scenarios A and B | |
Hsu et al. [35] | ML of RF, bagged classification, and regression tree model with AUROC compared with the conventional neonatal illness severity scoring systems | Clinical and laboratory data | RF model showed the highest AUROC (0.939) for the prediction of neonates with respiratory failure, and the bagged classification and regression tree model demonstrated the next best results (0.915). The AUCs of both models were significantly better than the traditional severity scoring systems | |
Do et al. [34] | ML methodsincluding ANN, RF, and SVM | Neonatal and maternal factors | The model performances of AUROC equaled Logistic regression 0.841, ANN 0.845, and RF 0.826. The exception was SVM 0.631 | |
Moreira et al. [36] | Model performance was assessed via AUROC | Accessible clinical variables, gathered in the first hour following delivery | The model consisted of three variables: birth weight, Apgar score at 5 min of age, and gestational age. This model had an AUROC of 76.9%, while birth weight and gestational age had an AUROC of 73.1% and 71.3% | |
Nascimento et al. [37] | A linguistic fuzzy model with minimum of Mamdani inference method | Neonatal birth weight and gestational age at delivery | The results were compared with experts’ opinions and the Fuzzy model was able to capture the expert knowledge with a strong correlation (r = 0.96) |
Challenges of AI | Areas of Improvement |
---|---|
Quality of the dataset | AI tools require high-quality data to be trained. Studies should address limitation including small sample sizes, improper management of missing information, and heterogeneity evaluation in various demographic subsets |
Model performance evaluation | Model performance should be continually evaluated on the entire dataset. Apart from the AUROC, additional performance metrics, such as the precision-recall curve, specificity/sensitivity, and calibration metrics should be assessed |
Clinical impact and external validation | External validation is crucial because, as in different dataset or in clinical practice, the tool’s performance may degrade due to an over-modeling of the training data.Also, the effectiveness of AI should be evaluated in terms of calibration and discrimination quality as well as patient outcomes and the clinical workflow |
Comprehending | Bed-side models should enhance intelligence, interpretability, and transparency |
Guidelines for critical evaluation, regulation, and oversight | methodological, critical appraisal, medicolegal problems, and necessary monitoring is required to guarantee the model’s safe and effective usage |
Ethics | Informed consent, bias, patient privacy, and allocation are among the ethical issues with health AI, and negotiating their solutions can be challenging. Important decisions in neonatology are often accompanied by a complex and difficult ethical component, and multidisciplinary methods are necessary for advancement |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Rallis, D.; Baltogianni, M.; Kapetaniou, K.; Giapros, V. Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit. BioMedInformatics 2024, 4, 1225-1248. https://doi.org/10.3390/biomedinformatics4020067
Rallis D, Baltogianni M, Kapetaniou K, Giapros V. Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit. BioMedInformatics. 2024; 4(2):1225-1248. https://doi.org/10.3390/biomedinformatics4020067
Chicago/Turabian StyleRallis, Dimitrios, Maria Baltogianni, Konstantina Kapetaniou, and Vasileios Giapros. 2024. "Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit" BioMedInformatics 4, no. 2: 1225-1248. https://doi.org/10.3390/biomedinformatics4020067