The current study showed that reinforcing the staple line during LSG has the following advantages: decreased incidence of postoperative leak and overall complications. More prospective studies with better evidence are needed.

Objective To investigate whether a computer-aided diagnosis (CAD) system based on a deep learning framework (deep learning-based CAD) improves the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasound (US). Materials and Methods B-mode US images were prospectively obtained for 253 breast masses (173 benign, 80 malignant) in 226 consecutive patients. Breast mass US findings were retrospectively analyzed by deep learning-based CAD and four radiologists. In predicting malignancy, the CAD results were dichotomized (possibly benign vs. possibly malignant). The radiologists independently assessed Breast Imaging Reporting and Data System final assessments for two datasets (US images alone or with CAD). For each dataset, the radiologists' final assessments were classified as positive (category 4a or higher) and negative (category 3 or lower). The diagnostic performances of the radiologists for the two datasets (US alone vs. US with CAD) were compared. Results When the CAD results were added to the US images, the radiologists showed significant improvement in specificity (range of all radiologists for US alone vs. US with CAD: 72.8–92.5% vs. 82.1–93.1%; p < 0.001), accuracy (77.9–88.9% vs. 86.2–90.9%; p = 0.038), and positive predictive value (PPV) (60.2–83.3% vs. 70.4–85.2%; p = 0.001). However, there were no significant changes in sensitivity (81.3–88.8% vs. 86.3–95.0%; p = 0.120) and negative predictive value (91.4–93.5% vs. 92.9–97.3%; p = 0.259). Conclusion Deep learning-based CAD could improve radiologists' diagnostic performance by increasing their specificity, accuracy, and PPV in differentiating between malignant and benign masses on breast US.

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