Laparoscopic surgery has many advantages over open surgery. At the same time, it is not without its risks. In this review, we discuss steps that could enhance the safety of laparoscopic surgery. Some of the important safety considerations are ruling out pregnancy in women of the childbearing age group; advanced discussion with the patient regarding unexpected intraoperative situations, and ensuring appropriate equipment is available. Important perioperative safety considerations include thromboprophylaxis; antibiotic prophylaxis; patient allergies; proper positioning of the patient, stack, and monitor(s); patient appropriate pneumoperitoneum; ergonomic port placement; use of lowest possible intra-abdominal pressure; use of additional five-millimetre (mm) ports as needed; safe use of energy devices and laparoscopic staplers; low threshold for a second opinion; backing out if unsafe to proceed; avoiding hand-over in the middle of the procedure; ensuring all planned procedures have been performed; inclusion of laparoscopic retrieval bags and specimens in the operating count; avoiding 10-15 mm ports for placement of drains; appropriate port closures; and use of long-acting local anaesthetic agents for analgesia. Important postoperative considerations include adequate analgesia; early ambulation; careful attention to early warning scores; and appropriate discharge advice.

Safety considerations in laparoscopic surgery: A narrative review

We developed a few-shot learning (FSL) framework for the diagnosis of osteopenia and osteoporosis in knee X-ray images. 

A few-shot learning framework for the diagnosis of osteopenia and osteoporosis using knee X-ray images

Mục tiêu: Đánh giá độ chính xác của thuật toán AI trong phát hiện tổn thương viêm thực quản trào ngược, khảo sát yếu tố liên quan đến việc bỏ sót, nhận nhầm. Phương pháp nghiên cứu: Mô tả cắt ngang. Thuật toán được kiểm chứng trên tập ảnh tĩnh bao gồm 1000 ảnh với các chế độ ánh sáng bằng cách so sánh phần khoanh vùng chuẩn của chuyên gia. Độ chính xác được đánh giá bằng độ nhạy, độ đặc hiệu, giá trị dự báo dương tính, giá trị dự báo âm tính. So sánh các tỷ lệ - xét mối liên quan được sử dụng để khảo sát những yếu tố liên quan đến tỉ lệ bỏ sót và nhận nhầm. Kết quả: Độ chính xác là 81,7%. Số lượng và kích thước tổn thương liên quan đến tỉ lệ bỏ sót. Tổn thương kèm theo, độ sạch liên quan đến tỉ lệ nhận nhầm. Kết luận: Thuật toán Yolov8 có độ chính xác tốt, có tiềm năng phát triển theo hướng đồng kiểm với bác sĩ nội soi trong khi soi, hậu kiểm sau nội soi, tham gia đào tạo y khoa với dữ liệu lớn. 

pdf/phat-trien-thuat-toan-tri-tue-nhan-tao-trong-phat-hien-viem-1xaw106uzx.pdf

Phát triển thuật toán trí tuệ nhân tạo trong phát hiện viêm thực quản trào ngược trên tập ảnh nội soi

FEMTL-DR: A feature-enhanced multi-task learning model for flexible drug recommendation

<title>Abstract</title> Objective The study aimed to evaluate the use decision support analysis for the prediction of extracorporeal shock wave lithotripsy (ESWL) efficacy and to analyze the factors influencing outcomes in patients who underwent ESWL using machine learning (ML) methods. Methods This retrospective study analyzed the clinical data, including preoperative CT images, of 302 patients who received a single ESWL session treatment for urinary tract stone (UTS) between May and October 2022 in the Department of Urology. The data was preprocessed and incorporated into an ML model, and the dataset was validated at a ratio of 4:1. The AUC and the confusion matrix were used to evaluate the predictive efficacy of the model. Results The CT image-based ML model predicting ESWL efficacy for UTS removal achieved an AUC of 0.86, precision of 88.33%, F1 score of 86.57%, sensitivity of 82.86%, and specificity of 88.89%. The model showed increased predictive accuracty for stones in different locations, with an AUC of 0.95 for kidney stones, 95.45% precision, 96% F1 score, 100% sensitivity, and 90% specificity. The AUC value for upper ureteral stones was 0.89, with 89.14% precision, 88.05% F1 score, 83.33% sensitivity, and 94.51% specificity, while that for mid-ureteral stones was 0.85, with 82.93% precision, 84.09% F1 score, 74% sensitivity, and 96.88% specificity, and the success rate of ESWL for lower ureteral stones was 100%, with an AUC of 1. Conclusions ML analysis was used to predict outcomes following ESWL treatment for UTS. The ML-based model was found to be approximately 86% accurate. The use of ML algorithms can provide matched insights to domain knowledge on effective and influential factors for the prediction of ESWL outcomes. 

Machine learning analysis of CT images for the prediction of extracorporeal shock wave lithotripsy efficacy

This study is to evaluate the feasibility of deep learning (DL) models in the multiclassification of reflux esophagitis (RE) endoscopic images, according to the Los Angeles (LA) classification for the first time. The images were divided into three groups, namely, normal, LA classification A + B, and LA C + D. The images from the HyperKvasir dataset and Suzhou hospital were divided into the training and validation datasets as a ratio of 4 : 1, while the images from Jintan hospital were the independent test set. The CNNs- or Transformer-architectures models (MobileNet, ResNet, Xception, EfficientNet, ViT, and ConvMixer) were transfer learning via Keras. The visualization of the models was proposed using Gradient-weighted Class Activation Mapping (Grad-CAM). Both in the validation set and the test set, the EfficientNet model showed the best performance as follows: accuracy (0.962 and 0.957), recall for LA A + B (0.970 and 0.925) and LA C + D (0.922 and 0.930), Marco-recall (0.946 and 0.928), Matthew's correlation coefficient (0.936 and 0.884), and Cohen's kappa (0.910 and 0.850), which was better than the other models and the endoscopists. According to the EfficientNet model, the Grad-CAM was plotted and highlighted the target lesions on the original images. This study developed a series of DL-based computer vision models with the interpretable Grad-CAM to evaluate the feasibility in the multiclassification of RE endoscopic images. It firstly suggests that DL-based classifiers show promise in the endoscopic diagnosis of esophagitis.

/pdf/development-and-validation-of-deep-learning-models-for-the-1zq2n1z6.pdf

Development and Validation of Deep Learning Models for the Multiclassification of Reflux Esophagitis Based on the Los Angeles Classification

Downregulated TRAF3IP2-AS1 promotes hepatocellular carcinoma progression through the miR-374a-5p/SEL1L1/RPL6 axis to enhance DNA damage repair.

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Development and Validation of Deep Learning Models for the Multiclassification of Reflux Esophagitis Based on the Los Angeles Classification

Downregulated TRAF3IP2-AS1 promotes hepatocellular carcinoma progression through the miR-374a-5p/SEL1L1/RPL6 axis to enhance DNA damage repair.