Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases
Abstract
:1. Introduction
2. Artificial Intelligence in ILDs
2.1. Overview and Rationale
2.2. Data Repositories
- OSIC data repository: launched by the Open Source Imaging Consortium, the OSIC repository contains approximately 1500 anonymized high-resolution CT scans alongside with clinical data for a wide range of interstitial lung diseases, primarily IPF [7]. The repository contains treatment information, as well as follow-up data and mortality. It was built by a collaboration of experts in the fields of pulmonology, radiology and AI, aiming primarily to facilitate the role of the latter in patient care and precision medicine. A subset of the OSIC data repository containing information regarding pulmonary fibrosis progression has become available from Kaggle, for researchers to develop tools and algorithms aiming to predict lung function decline. The subset repository contains 200 cases with approximately 1–2 years of follow-up.
- ILD Database from medGIFT [8]: a publicly available multimedia collection of ILD cases built from the University Hospitals of Geneva. The database contains high-resolution CT scans with annotated regions of pathological lung areas coupled with clinical parameters from patients with pathologically proven diagnoses of ILDs. Overall, the library contains data from 128 patients affected with 1 of the 13 ILD diseases.
- ILDgenDB [9]: an integrated genetic knowledge resource for interstitial lung diseases, which, as the name implies, contains genetic data about several ILDs. This resource contains literature-curated disease candidate genes enriched with regulatory elements, as well as single nucleotide polymorphisms (SNPs) that have been associated with specific ILDs. For this propose, ILDgenDB is enriched with information from multiple popular genetic resources such as GAD (Genetic Association Database), OMIM (Online Mendelian Inheritance in Man) and GeneCards. The objective of this resource is to pinpoint potential genetic targets related to the pathogenesis, diagnosis, monitoring and treatment of ILDs.
- ILDGDB [10]: a similarly oriented repository as ILDgenDB, utilizing genomic, transcriptomic, proteomic and drug information for interstitial lung diseases. ILDGDB incorporates 2018 entries for 20 ILDs and over 600 genes; its purpose is to decipher gene mechanisms that take place in ILDs.
3. AI Applications in ILD Research
3.1. Screening
3.2. Diagnosis and Classification
3.3. Prognosis
4. Discussion
Future Directions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Author(s) | Scope | Dataset | Type of Data | Performance |
---|---|---|---|---|
Bermejo-Peláez et al. [11] | Screening | 208 CT scans | Imaging | Sensitivity: 91.41% |
Agarwala et al. [12] | Screening | 168 CT scans | Imaging | Success rate: 85.3% |
Kim et al. [13] | Screening | 336 participants | Imaging | Accuracy: 90.5% |
Nishikiori et al. [14] | Screening | 1159 chest X-rays | Imaging | AUC = 0.979 |
Onishchenko et al. [15] | Screening | 2,983,215 participants | Electronic Health Records | AUC > 0.840 |
Axelsson et al. [16] | Screening | >10,000 patients | Proteins | - |
Pawar and Talbar [17] | Diagnosis & classification | 108 CT scans | Imaging | Accuracy: 89.39% |
Huang et al. [18] | Diagnosis & classification | 108 CT scans | Imaging | F1-score > 0.96 |
Chloe et al. [19] | Diagnosis & classification | 300 patients | Imaging | Accuracy: 60.9% |
Koo et al. [20] | Diagnosis & classification | 1085 patients | Imaging | AUC > 0.900 |
Furukawa et al. [21] | Diagnosis & classification | 1068 patients | Imaging | Accuracy: 83.6% |
Christe et al. [22] | Diagnosis & classification | 105 patients | Imaging | Accuracy: 81% |
Bratt et al. [23] | Diagnosis & classification | 1239 patients | Imaging | AUC = 0.870 |
Yang et al. [24] | Diagnosis & classification | 1760 chest X-rays | Imaging | Accuracy: 92.46% |
Horimasu et al. [25] | Diagnosis & classification | 60 patients | Auscultation | Accuracy: 75% |
Plantier et al. [26] | Diagnosis & classification | 150 patients | Volatile Organic Compounds | Accuracy: 77.5% |
Zhang et al. [27] | Diagnosis & classification | 300 patients | Gene Expression | - |
Li et al. [28] | Diagnosis & classification | 600 patients | Gene Expression | AUC = 0.856 |
Kim et al. [29] | Prognosis | 192 patients | Imaging | - |
Handa et al. [30] | Prognosis | 465 patients | Imaging | - |
Budzikowski et al. [31] | Prognosis | 169 patients | Imaging and Genomic | - |
Liang et al. [32] | Prognosis | 116 patients | Imaging | AUC = 0.870 |
Aoki et al. [33] | Prognosis | 104 patients | Imaging | - |
Bowman et al. [34] | Prognosis | 589 patients | Proteomic | Sensitivity: 90% |
Mayr et al. [35] | Prognosis | 124 patients | Proteomic | Accuracy: 83% |
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Exarchos, K.P.; Gkrepi, G.; Kostikas, K.; Gogali, A. Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases. Diagnostics 2023, 13, 2303. https://doi.org/10.3390/diagnostics13132303
Exarchos KP, Gkrepi G, Kostikas K, Gogali A. Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases. Diagnostics. 2023; 13(13):2303. https://doi.org/10.3390/diagnostics13132303
Chicago/Turabian StyleExarchos, Konstantinos P., Georgia Gkrepi, Konstantinos Kostikas, and Athena Gogali. 2023. "Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases" Diagnostics 13, no. 13: 2303. https://doi.org/10.3390/diagnostics13132303
APA StyleExarchos, K. P., Gkrepi, G., Kostikas, K., & Gogali, A. (2023). Recent Advances of Artificial Intelligence Applications in Interstitial Lung Diseases. Diagnostics, 13(13), 2303. https://doi.org/10.3390/diagnostics13132303