| Type of Data | Sample | Objective | Model Design | Results | Therapeutic Area | Paper |
Mammography | Mammography images | 45,000 images | Detect malign solid lesions and prevent overtreatment in false positives [2] | CNN | AUC of 0.90 | Oncology | [89] |
Mammography | 667 benign and 333 malignant | Mammography diagnosis of early malignant breast | Stacked AE | Accuracy of 0.89 | Oncology | [96] | |
Digital Mammography images and the biopsy result of the lesions [2] | 1000 malignant masses and 600 cysts images and their biopsy [2] | Discriminate benign cysts from malignant masses | CNN | AUC of 0.80 | Oncology | [97] | |
Mammography images | 840 images of mammograms from 210 different patients | Breast arterial calcification on mammograms classifier to evaluate the risk of coronary disease [2] | CNN | Misclassfied cases of 6% | Cardiovascular | [101] | |
Digital mammograms | 661 from 444 patients | Computer automated estimation of breast percentage density [2] | CNN | AUC of 0.981 | Oncology | [151] | |
Mammography images | Mammograms from 604 women | Segment areas of dense fibroglandular tissue in the breast [2] | CNN | Accuracy of 0.66 | Oncology | [116] | |
Digital mammograms images | 29,107 left mediolateral oblique, right mediolateral oblique, left cranial-caudal and right cranial-caudal mammograms images | Probability of cancer on mammograms [2] | CNN | AUC of 0.90 | Oncology | [121] | |
Ultrasound | Image of the heart 2D | 400 images with five different heart diseases and 80 normal echocardiogram images | Segment left ventricle images with greater precision | Deep belief networks | Hammoude distance of 0.80 | Cardiovascular | [152] |
Ultrasound imaging | 306 malignant and 136 benign tumors images | CAD system to detect and differentiate breast lesions with ultrasound | CNNs inspired in AlexNet, U-Net and LeNet | Best F-measure of 0.91 and 0.89 depending on the data | Oncology | [2] [24] | |
Transesophageal ultrasound volume and 3D geometry of the aortic valve images | 3795 volumes from the aortic valves from 150 patients | Diagnose, stratification and treatment planning for patients with aortic valve pathologies | Marginal space deep learning | Position error of 1.66 mms and mean corner distance error of 3.29 mms | Cardiovascular | [2] [84] |