The incidence of orthopedic trauma-related diseases keeps rising annually, which brings an urgent need to optimize the diagnostic and treatment processes to enhance treatment efficiency and improve patients′ prognosis. Traditional diagnostic methods in traumatic orthopedics primarily rely on manual film interpretation and classification, resulting in a substantial workload for physicians and consequently a low efficiency. Furthermore, during multidisciplinary consultations and cross-hospital referrals for patients with orthopedic trauma, accessing medical records and facilitating information exchange can be challenging, leading to delays in surgical intervention. The rapid advancement of artificial intelligence (AI) technology, characterized by feature engineering, artificial neural networks, and deep learning, has transformed the landscape of rapid diagnosis and precision treatment for orthopedic trauma. Nonetheless, centralized storage during task training poses risks of privacy disclosure and security concerns that impede the widespread application of AI models. In contrast, the decentralized nature of blockchain technology offers a secure operational environment for AI-driven diagnostics and treatments and the integration of blockchain and AI can deliver more accurate, efficient, and safe services for patients with orthopedic trauma. Currently, challenges remain in the inter-institutional sharing of data, constant phenomenon of data silos and absence of standardized protocols for developing collaborative models in clinical settings. To address these challenges, the authors reviewed the research advancements in integrated application of blockchain technology and artificial intelligence in diagnosing and treating orthopedic trauma, aiming to provide insights into the development of a digital diagnostic system tailored to this field in China.

The incidence of orthopedic trauma-related diseases keeps rising annually, which brings an urgent need to optimize the diagnostic and treatment processes to enhance treatment efficiency and improve patients′ prognosis. Traditional diagnostic methods in traumatic orthopedics primarily rely on manual film interpretation and classification, resulting in a substantial workload for physicians and consequently a low efficiency. Furthermore, during multidisciplinary consultations and cross-hospital referrals for patients with orthopedic trauma, accessing medical records and facilitating information exchange can be challenging, leading to delays in surgical intervention. The rapid advancement of artificial intelligence (AI) technology, characterized by feature engineering, artificial neural networks, and deep learning, has transformed the landscape of rapid diagnosis and precision treatment for orthopedic trauma. Nonetheless, centralized storage during task training poses risks of privacy disclosure and security concerns that impede the widespread application of AI models. In contrast, the decentralized nature of blockchain technology offers a secure operational environment for AI-driven diagnostics and treatments and the integration of blockchain and AI can deliver more accurate, efficient, and safe services for patients with orthopedic trauma. Currently, challenges remain in the inter-institutional sharing of data, constant phenomenon of data silos and absence of standardized protocols for developing collaborative models in clinical settings. To address these challenges, the authors reviewed the research advancements in integrated application of blockchain technology and artificial intelligence in diagnosing and treating orthopedic trauma, aiming to provide insights into the development of a digital diagnostic system tailored to this field in China.

Objective:To construct a real-time artificial intelligence (AI)-assisted endoscepic diagnosis system based on YOLO v3 algorithm, and to evaluate its ability of detecting focal gastric lesions in gastroscopy.Methods:A total of 5 488 white light gastroscopic images (2 733 images with gastric focal lesions and 2 755 images without gastric focal lesions) from June to November 2019 and videos of 92 cases (288 168 clear stomach frames) from May to June 2020 at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University were retrospectively collected for AI System test. A total of 3 997 prospective consecutive patients undergoing gastroscopy at the Digestive Endoscopy Center of Renmin Hospital of Wuhan University from July 6, 2020 to November 27, 2020 and May 6, 2021 to August 2, 2021 were enrolled to assess the clinical applicability of AI System. When AI System recognized an abnormal lesion, it marked the lesion with a blue box as a warning. The ability to identify focal gastric lesions and the frequency and causes of false positives and false negatives of AI System were statistically analyzed.Results:In the image test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 92.3% (5 064/5 488), 95.0% (2 597/2 733), 89.5% (2 467/ 2 755), 90.0% (2 597/2 885) and 94.8% (2 467/2 603), respectively. In the video test set, the accuracy, the sensitivity, the specificity, the positive predictive value and the negative predictive value of AI System were 95.4% (274 792/288 168), 95.2% (109 727/115 287), 95.5% (165 065/172 881), 93.4% (109 727/117 543) and 96.7% (165 065/170 625), respectively. In clinical application, the detection rate of local gastric lesions by AI System was 93.0% (6 830/7 344). A total of 514 focal gastric lesions were missed by AI System. The main reasons were punctate erosions (48.8%, 251/514), diminutive xanthomas (22.8%, 117/514) and diminutive polyps (21.4%, 110/514). The mean number of false positives per gastroscopy was 2 (1, 4), most of which were due to normal mucosa folds (50.2%, 5 635/11 225), bubbles and mucus (35.0%, 3 928/11 225), and liquid deposited in the fundus (9.1%, 1 021/11 225).Conclusion:The application of AI System can increase the detection rate of focal gastric lesions.