Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education

TL;DR

An artificial intelligence system, trained on over 45,139 ultrasound images from 9,215 fetuses across 22 hospitals, can diagnose fetal orofacial clefts with sensitivity and specificity exceeding 93% and 95% respectively, matching the performance of senior radiologists and substantially outperforming junior radiologists.

Abstract

Orofacial clefts are among the most common congenital craniofacial abnormalities, yet accurate prenatal detection remains challenging due to the scarcity of experienced specialists and the relative rarity of the condition. Early and reliable diagnosis is essential to enable timely clinical intervention and reduce associated morbidity. Here we show that an artificial intelligence system, trained on over 45,139 ultrasound images from 9,215 fetuses across 22 hospitals, can diagnose fetal orofacial clefts with sensitivity and specificity exceeding 93% and 95% respectively, matching the performance of senior radiologists and substantially outperforming junior radiologists. When used as a medical copilot, the system raises junior radiologists' sensitivity by more than 6%. Beyond direct diagnostic assistance, the system also accelerates the development of clinical expertise. A pilot study involving 24 radiologists and trainees demonstrated that the model can improve the expertise development for rare conditions. This dual-purpose approach offers a scalable solution for improving both diagnostic accuracy and specialist training in settings where experienced radiologists are scarce.

Figures (10)

• Figure 1: Design of the AIOC system. (a) Development of the AIOC system. It is based on a dual-branch network, integrates detection (YOLOX) and classification (MILA) branches for accurate diagnosis of OC and healthy cases (i.e., CL, CLP, and Control). It also visualizes view classification (i.e., NLV, CLV, NAPV, CAPV) and key structures (i.e., upper lip (purple), alveolar ridge (yellow), cleft lip (blue), cleft alveolus (red), and cleft palate (green)). (b) The internal dataset, OC-6000. (c) The external datasets, OC-GT3000 and OC-Early. (d) Case-based quantitative evaluation of AIOC and a comparative experiment with radiologists. CL: cleft lip; CLP: cleft lip and palate; CLV: cleft lip view; NLV: normal lip view; NAPV: normal alveolus and palate view; CAPV: cleft alveolus and palate view.
• Figure 2: Medical training pilot study design. (a) Overview of the pilot study workflow with theoretical lecture, randomized allocation to AI-augmented or traditional training groups, and four training-and-exam cycles. (b) AI-augmented training using 20 cases from OC-6000 with AIOC system assistance. The participants will be provided with additional view categories, bounding boxes for anatomical structures, and AI diagnostic recommendations, compared with the traditional training group (c). Traditional training using the same 20 cases without AI assistance. (d) Learning retention evaluation using 200 fixed cases from OC-3000. (e) Generalization assessment using 100 random cases from OC-3000 to test diagnostic skill transferability. T-TG: traditional training groups; AI-TG: AI training groups; CL: cleft lip; CLP: cleft lip and palate.
• Figure 3: Performance of the AIOC system on the OC-6000 testing dataset. (a) Confusion matrix, (b) ROC curve, and (c) t-SNE plot for case-based diagnosis (Control (blue), CL (orange), and CLP (green)). (d) Specific examples of Control, CL, and CLP cases, comparing the views, Gray-CAM results of AIOC, and the annotated key structures. Red boxes highlight cleft lip, cleft alveolus, or cleft palate abnormalities, while green boxes indicate normal tissue regions in control cases. CL: cleft lip; CLP: cleft lip and palate; CLV: cleft lip view; NLV: normal lip view; NAPV: normal alveolus and palate view; CAPV: cleft alveolus and palate view; AUC: area under the curve; ROC: receiver operating characteristic.
• Figure 4: Fetal case-based diagnosis results of the AIOC, senior&junior radiologists, and junior radiologists assisted by AIOC (Junior-AIOC) on the OC-GT3000 dataset. (a) Confusion matrix and (b) ROC curve for AIOC, Senior, Junior, and Junior-AIOC diagnosis. (c) Bar plot showing F1 scores (18-28 weeks) for AIOC and radiologists (Senior&Junior). Due to the small number of cases in the 18-20 and 25-28 week groups, they were combined for analysis. (d) Representative examples of CLP, CL, and Control cases and their diagnosed results by AIOC, junior radiologists, senior radiologists, and junior radiologists assisted by AIOC. Each case includes multiple ultrasound views (left) and their zoom-in views of the region of interest (right). Red boxes highlight cleft lip, cleft alveolus, or cleft palate abnormalities, while green boxes indicate normal tissue regions in control cases. CL: cleft lip; CLP: cleft lip and palate; AUC: area under the curve; ROC: receiver operating characteristic.
• Figure 5: Fetal case-based diagnosis results from the AIOC system on the OC-Early dataset. (a) Confusion matrix for fetal case-based diagnosis. (b) Specific cases: A CL case at 17 weeks of gestational age, with AIOC providing the correct diagnosis and Gray-CAM visualizing the diagnostic details. A CLP case at 15 weeks of gestational age, with AIOC also providing the correct diagnosis and visualizing the details. CL: cleft lip; CLP: cleft lip and palate.