Artificial Intelligence in Emergency Radiology: Where Are We Going?
Abstract
:1. Introduction
2. AI basic Terminology
3. AI in Image Acquisition
4. Worklist Prioritization
5. Automatic Detection
5.1. Stroke
5.2. Trauma and Bone Fractures
5.3. Abdominal Emergencies
5.3.1. Abdominal Trauma
5.3.2. Small Bowel Occlusion
5.3.3. Intussusception
5.4. Chest Emergencies
5.4.1. Pulmonary Embolism
5.4.2. Pneumonia
6. Smart Reporting
7. Challenges and Perspectives
8. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Type of ML | Mechanism | Type of Data Provided | Tasks for which the Mechanism can be used | Examples of Models |
---|---|---|---|---|
Supervised learning (SL) | The algorithm, provided with tuples (x,y) of input (labeled) and output (unlabeled), infers the relations that map the data. | Labeled data | Classification task; Regression task |
|
Unsupervised learning (UL) | The algorithm exhibits self-organization, to capture hidden patterns in data. | Unlabeled data | Clustering Association; Anomalies detection |
|
Semi-supervised learning (SSL) | The algorithm is placed between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data). | Mostly unlabeled data, with a small amount of labeled data. | Transductive task (infer the correct labels for the given unlabeled data) or inductive tasks (infer the correct mapping from x to y). |
|
Reinforcement learning (RL) | The algorithm is created with a goal and a set of rules. The algorithm tends to maximize the "reward function" or reinforcement signals, to achieve the goal. | Not needing labeled input/output pairs to be presented; only a numerical performance score is given as guidance. | Good for modeling complex-task decision-making processes, such as economics and game theory under bounded rationality. |
|
Section | Authors | Main Application | Technique | Findings |
---|---|---|---|---|
Neuroradiology | Matsoukas et al. [43] | Intracranial hemorrhages | CT | Sensitivity, specificity, and accuracy of 92.06%, 93.54%, and 93.46%. |
Cerebral microbleeds | CT | Sensitivity, specificity, and accuracy of 91.6%, 93.9%, and 92.7%. | ||
Rava et al. [44] | Intracranial hemorrhages | CT | Sensitivity of 93%, specificity of 93%, a positive predicting value of 85%, and a negative predicting value of 98%. | |
McLouth et al. [46] | Large vessel occlusion | CT | Accuracy of 98.1%, sensitivity of 98.1%, specificity of 98.2%. | |
MSK | Cheng et al. [54] | Femoral fractures detection | X-ray | AUC of 0.98, accuracy of 91%, sensitivity of 98%, specificity of 84%, and an F1 score of 0.916. |
Jones et al. [55] | Fractures detection in 16 anatomical regions. | X-ray | AUC of 0.974, sensitivity of 95.2%, specificity of 81.3%, a positive predictive value (PPV) of 47.4%, and a negative predictive value (NPV) of 99.0%. | |
Minamoto et al. [56] | Anterior Cruciate Ligament lesion | MRI | Ssensitivity of 91%, specificity of 86%, accuracy of 88.5%, a positive predictive value of 87.0%, and a negative predictive value of 91.0%. | |
Bien et al. [58] | Anterior Cruciate Ligament lesion | MRI | AUC of 0.965, when compared to three musculoskeletal radiologists. | |
Meniscal tears | MRI | AUC of 0.965, when compared to three musculoskeletal radiologists. | ||
Liu et al. [60] | Meniscal tears | MRI | Sensitivity and sensibility of 84.1% and 85.2%, respectively, for evaluation 1, and of 80.5% and 87.9%, respectively, for evaluation 2. Areas under the ROC curve were 0.917 and 0.914 for evaluations 1 and 2, respectively. | |
Roblot et al. [61] | Meniscal tears | MRI | AUC of 0.92 for the detection of the position of the two meniscal horns, of 0.94 for the presence of a meniscal tear, of 0.83 for determining the orientation of the tear, and a final weighted AUC of 0.90. | |
Abdominal | Cheng et al. [71] | Ascites in the Morison pouch | Ultrasound | 0.961 for accuracy, 0.976 for sensitivity, 0.947 for specificity in the validation set, and 0.967, 0.985, and 0.913 in the test set, respectively. |
Drezin et al. [72] | Measurement of the liver parenchymal disruption index | CT | Accuracy of 0.84 | |
Kim et al. [77] | Small bowel occlusion | X-ray | AUC of 0.961, sensitivity of 91%, specificity of 93%. | |
Goyal et al. [78] | Closed-loop small bowel occlusion | CT | AUC of 0.73, sensitivity of 0.72, specificity of 0.8, accuracy of 0.73. | |
Chest | Cheik et al. [86] | Pulmonary embolism | CT | The AI had the best sensitivity and negative predictive values (92.6% vs. 90%, and 98.6% vs. 98.1%, respectively), whereas radiologists had the highest specificity and positive predictive values (99.1% vs. 95.8%, and 95% vs. 80.4%, respectively). |
Batra et al. [87] | Incidental pulmonary embolism | CT | AI had a lower positive predictive value (86.8% versus 97.3%, p = 0.03) and specificity (99.8% vs. 100.0%, p = 0.045) vs. radiologists. | |
Soffer et al. [88] | Pulmonary embolism | CT | Sensitivity and specificity were 0.88 and 0.86, respectively. | |
Xiong et al. [92] | COVID-19 pneumonia | CT | Accuracy of 96%, sensitivity of 95%, and specificity of 96%. | |
Rajpurkar et al. [93] | Pneumonia | X-ray | F1 score of 0.435. |
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Cellina, M.; Cè, M.; Irmici, G.; Ascenti, V.; Caloro, E.; Bianchi, L.; Pellegrino, G.; D’Amico, N.; Papa, S.; Carrafiello, G. Artificial Intelligence in Emergency Radiology: Where Are We Going? Diagnostics 2022, 12, 3223. https://doi.org/10.3390/diagnostics12123223
Cellina M, Cè M, Irmici G, Ascenti V, Caloro E, Bianchi L, Pellegrino G, D’Amico N, Papa S, Carrafiello G. Artificial Intelligence in Emergency Radiology: Where Are We Going? Diagnostics. 2022; 12(12):3223. https://doi.org/10.3390/diagnostics12123223
Chicago/Turabian StyleCellina, Michaela, Maurizio Cè, Giovanni Irmici, Velio Ascenti, Elena Caloro, Lorenzo Bianchi, Giuseppe Pellegrino, Natascha D’Amico, Sergio Papa, and Gianpaolo Carrafiello. 2022. "Artificial Intelligence in Emergency Radiology: Where Are We Going?" Diagnostics 12, no. 12: 3223. https://doi.org/10.3390/diagnostics12123223
APA StyleCellina, M., Cè, M., Irmici, G., Ascenti, V., Caloro, E., Bianchi, L., Pellegrino, G., D’Amico, N., Papa, S., & Carrafiello, G. (2022). Artificial Intelligence in Emergency Radiology: Where Are We Going? Diagnostics, 12(12), 3223. https://doi.org/10.3390/diagnostics12123223