Ultrasound in medicine & biology · 2026
Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3-D Ultrasound.
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Xu Yueyue, Huang Yuhao, Deng Jiaxiao, Zhang Yuanji, Zhang Haoming, Qu Jiajia, Zheng Shiying, Tang Xiaomei, Chen Haining, Chen Chengcai, Wu Yiyi, Yang Xin, Ni Dong, Zheng Hongyu
Аннотация
OBJECTIVE: This study aimed to develop an intelligent framework, termed CUA-Net, for the automated classification of congenital uterine anomalies (CUAs) without the requirement of coronal plane reconstruction, and to evaluate its clinical applicability.
METHODS: CUA-Net was built on 3-D ResNet-18, equipped with a dynamic data re-sampling strategy to mitigate the data imbalance issue and a hard sample mining technique to fully learn from difficult cases through loss adjustment. We further proposed self-supervised reconstruction to comprehensively explore volumes and online data augmentation to refine incorrect predictions and enhance the model's generalization. We used internal and external test sets to compare CUA-Net with different deep learning methods as well as junior/senior sonographers. The evaluation metrics included accuracy, precision, recall, F1-score, micro-area under the curve (AUC) and macro-AUC.
RESULTS: CUA-Net exhibited satisfactory performance in both the internal and external test sets. In the internal cohort, the model achieved an accuracy of 93.88%, precision of 87.01%, recall of 95.92%, F1-score of 88.09%, micro-AUC of 0.9982 and macro-AUC of 0.9997. In the external set, it maintained good performance with an 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. CUA-Net outperformed junior sonographers across all performance indicators and achieved performance comparable to that of senior sonographers across most metrics.
CONCLUSION: 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 to optimize clinical workflows and support more standardized diagnosis.
METHODS: CUA-Net was built on 3-D ResNet-18, equipped with a dynamic data re-sampling strategy to mitigate the data imbalance issue and a hard sample mining technique to fully learn from difficult cases through loss adjustment. We further proposed self-supervised reconstruction to comprehensively explore volumes and online data augmentation to refine incorrect predictions and enhance the model's generalization. We used internal and external test sets to compare CUA-Net with different deep learning methods as well as junior/senior sonographers. The evaluation metrics included accuracy, precision, recall, F1-score, micro-area under the curve (AUC) and macro-AUC.
RESULTS: CUA-Net exhibited satisfactory performance in both the internal and external test sets. In the internal cohort, the model achieved an accuracy of 93.88%, precision of 87.01%, recall of 95.92%, F1-score of 88.09%, micro-AUC of 0.9982 and macro-AUC of 0.9997. In the external set, it maintained good performance with an 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. CUA-Net outperformed junior sonographers across all performance indicators and achieved performance comparable to that of senior sonographers across most metrics.
CONCLUSION: 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 to optimize clinical workflows and support more standardized diagnosis.