Integrating blockchain technology with artificial intelligence for the diagnosis of tibial plateau fractures.

Purpose: The application of artificial intelligence (AI) in healthcare has seen widespread implementation, with numerous studies highlighting the development of robust algorithms. However, limited attention has been given to the secure utilization of raw data for medical model training, and its subsequent impact on clinical decision-making and real-world applications. This study aims to assess the feasibility and effectiveness of an advanced diagnostic model that integrates blockchain technology and AI for the identification of tibial plateau fractures (TPFs) in emergency settings.

Method: In this study, blockchain technology was utilized to construct a distributed database for trauma orthopedics, images collected from three independent hospitals for model training, testing, and internal validation. Then, a distributed network combining blockchain and deep learning was developed for the detection of TPFs, with model parameters aggregated across multiple nodes to enhance accuracy. The model's performance was comprehensively evaluated using metrics including accuracy, sensitivity, specificity, F1 score, and the area under the receiver operating characteristic curve (AUC). In addition, the performance of the centralized model, the distributed AI model, clinical orthopedic attending physicians, and AI-assisted attending physicians was tested on an external validation dataset.

Results: In the testing set, the accuracy of our distributed model was 0.9603 [95% CI (0.9598, 0.9605)] and the AUC was 0.9911 [95% CI (0.9893, 0.9915)] for TPF detection. In the external validation set, the accuracy reached 0.9636 [95% CI (0.9388, 0.9762)], was slightly higher than that of the centralized YOLOv8n model at 0.9632 [95% CI (0.9387, 0.9755)] (p > 0.05), and exceeded the orthopedic physician at 0.9291 [95% CI (0.9002, 0.9482)] and radiology attending physician at 0.9175 [95% CI (0.8891, 0.9393)], with a statistically significant difference (p < 0.05). Additionally, the centralized model (4.99 ± 0.01 min) had shorter diagnosis times compared to the orthopedic attending physician (25.45 ± 1.92 min) and the radiology attending physician (26.21 ± 1.20 min), with a statistically significant difference (p < 0.05).

Conclusion: The model based on the integration of blockchain technology and AI can realize safe, collaborative, and convenient assisted diagnosis of TPF. Through the aggregation of training parameters by decentralized algorithms, it can achieve model construction without data leaving the hospital and may exert clinical application value in the emergency settings.