Table 2.
This table sums up the main results from the studies investigating the different applications of AI tools for automatic disease detection.
| 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. |
AI = artificial intelligence; AUC = area under the curve.