Unveiling new patterns: A surgical deep learning model for intestinal obstruction management.

BACKGROUND: Swift and accurate decision-making is pivotal in managing intestinal obstructions. This study aims to integrate deep learning and surgical expertise to enhance decision-making in intestinal obstruction cases. METHODS: We developed a deep learning model based on the YOLOv8 framework, trained on a dataset of 700 images categorised into operated and non-operated groups, with surgical outcomes as ground truth. The model's performance was evaluated through standard metrics. RESULTS: At a confidence threshold of 0.5, the model demonstrated sensitivity of 83.33%, specificity of 78.26%, precision of 81.7%, recall of 75.1%, and mAP@0.5 of 0.831. CONCLUSIONS: The model exhibited promising outcomes in distinguishing operative and nonoperative management cases. The fusion of deep learning with surgical expertise enriches decision-making in intestinal obstruction management. The proposed model can assist surgeons in intricate scenarios such as intestinal obstruction management and promotes the synergy between technology and clinical acumen for advancing patient care.

DraiNet: AI-driven decision support in pneumothorax and pleural effusion management.

OBJECTIVE: This study presents DraiNet, a deep learning model developed to detect pneumothorax and pleural effusion in pediatric patients and aid in assessing the necessity for tube thoracostomy. The primary goal is to utilize DraiNet as a decision support tool to enhance clinical decision-making in the management of these conditions. METHODS: DraiNet was trained on a diverse dataset of pediatric CT scans, carefully annotated by experienced surgeons. The model incorporated advanced object detection techniques and underwent evaluation using standard metrics, such as mean Average Precision (mAP), to assess its performance. RESULTS: DraiNet achieved an impressive mAP score of 0.964, demonstrating high accuracy in detecting and precisely localizing abnormalities associated with pneumothorax and pleural effusion. The model's precision and recall further confirmed its ability to effectively predict positive cases. CONCLUSION: The integration of DraiNet as an AI-driven decision support system marks a significant advancement in pediatric healthcare. By combining deep learning algorithms with clinical expertise, DraiNet provides a valuable tool for non-surgical teams and emergency room doctors, aiding them in making informed decisions about surgical interventions. With its remarkable mAP score of 0.964, DraiNet has the potential to enhance patient outcomes and optimize the management of critical conditions, including pneumothorax and pleural effusion.