Deep learning system improves diagnosis of uterine anomalies in 3d ultrasound
Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound
Computer Vision and Pattern Recognition
Summary
Diagnosing congenital uterine anomalies using 3D ultrasound images is important but can be challenging for doctors. The authors developed a deep learning system called CUA-Net that automatically classifies these anomalies without needing special image reconstructions. They tested CUA-Net on multiple data sets and found it performs better than less experienced doctors and nearly as well as experts. This system could help standardize and speed up the diagnosis process in clinical practice.
What this means in practice
- •For clinical sonographers: Assist sonographers in accurately classifying uterine anomalies from 3D ultrasound scans without extra image reconstruction steps.
- •For healthcare ai product developers: Integrate CUA-Net into ultrasound diagnostic tools to improve automated detection and classification of congenital uterine anomalies.$Commercial implications: Enables development of advanced ultrasound diagnostic software to sell to hospitals and clinics for improved gynecological care.
Authors
Yueyue Xu, Yuhao Huang, Jiaxiao Deng, Yuanji Zhang, Haoming Zhang, Jiajia Qu, Shiying Zheng, Xiaomei Tang, Haining Chen, Chengcai Chen, Yiyi Wu, Xin Yang, Dong Ni
Abstract
Objective: To develop an intelligent framework, termed CUA-Net, for the automated classification of congenital uterine anomalies (CUA) without requiring coronal plane reconstruction, and to evaluate its clinical applicability. Methods: CUA-Net was built on 3D ResNet-18, equipped with a dynamic data resampling strategy to mitigate the data imbalance issue and a hard sample mining technique to fully learn from the difficult cases by loss adjustment. We further proposed the self-supervised reconstruction to comprehensively explore the volumes and the online data augmentation to refine the wrong predictions and enhance the model's generalization. We compared the CUA-Net with different deep-learning methods and junior/senior sonographers in the testing set. The evaluation metrics included accuracy, precision, recall, F1-score, micro-AUC, and macro-AUC. Results: The proposed CUA-Net exhibited satisfactory performance in both internal and external test sets. In the internal cohort, the model achieved accuracy of 93.88%, precision of 87.01%, recall of 95.92%, F1-score of 88.09%, and micro-AUC of 0.9982 and macro-AUC of 0.9997. In the external set, it maintained good performance with accuracy of 91.52%, precision of 83.27%, recall of 88.63%, F1-score of 81.49%, micro-AUC of 0.9945 and macro-AUC of 0.9990. Our CUA-Net outperformed the junior sonographers across all performance indicators and achieved performance comparable to that of the senior sonographers across most metrics. Conclusion: The CUA-Net demonstrates favorable accuracy and generalizability in classifying common CUA categories, while showing preliminary potential for recognizing less prevalent anomalies. These capabilities may help optimize clinical workflows and support more standardized diagnosis.