Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education

• URL: http://arxiv.org/abs/2603.06522v1
• Date: Fri, 06 Mar 2026 18:06:20 GMT
• Title: Artificial Intelligence for Detecting Fetal Orofacial Clefts and Advancing Medical Education
• Abstract summary: Orofacial clefts are among the most common congenital craniofacial abnormalities.<n>We show that an artificial intelligence system can diagnose fetal orofacial clefts with sensitivity and specificity exceeding 93% and 95% respectively.
• Score: 50.132847182375514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Automated detection of pediatric congenital heart disease from phonocardiograms using deep and handcrafted feature fusion [7.150060950962671]
  Congenital heart disease (CHD) is the most common type of birth defect, impacting about 1% of live births worldwide.<n>We present a new method for a more accessible diagnostic modality, the digital stethoscope, to detect CHDs.<n>Our method is based on deep feature fusion, integrating deep and handcrafted features for the automated early detection of CHDs.
  arXiv  Detail & Related papers  (2026-04-15T00:31:44Z)
• A DeepSeek-Powered AI System for Automated Chest Radiograph Interpretation in Clinical Practice [83.11942224668127]
  Janus-Pro-CXR (1B) is a chest X-ray interpretation system based on DeepSeek Janus-Pro model.<n>Our system outperforms state-of-the-art X-ray report generation models in automated report generation.
  arXiv  Detail & Related papers  (2025-12-23T13:26:13Z)
• An Explainable Hybrid AI Framework for Enhanced Tuberculosis and Symptom Detection [55.35661671061754]
  Tuberculosis remains a critical global health issue, particularly in resource-limited and remote areas.<n>We propose a framework which enhances disease and symptom detection on chest X-rays by integrating two supervised heads and a self-supervised head.<n>Our model achieves an accuracy of 98.85% for distinguishing between COVID-19, tuberculosis, and normal cases, and a macro-F1 score of 90.09% for multilabel symptom detection.
  arXiv  Detail & Related papers  (2025-10-21T17:18:55Z)
• An Agentic System for Rare Disease Diagnosis with Traceable Reasoning [69.46279475491164]
  We introduce DeepRare, the first rare disease diagnosis agentic system powered by a large language model (LLM)<n>DeepRare generates ranked diagnostic hypotheses for rare diseases, each accompanied by a transparent chain of reasoning.<n>The system demonstrates exceptional diagnostic performance among 2,919 diseases, achieving 100% accuracy for 1013 diseases.
  arXiv  Detail & Related papers  (2025-06-25T13:42:26Z)
• Privacy-Preserving Federated Foundation Model for Generalist Ultrasound Artificial Intelligence [83.02106623401885]
  We present UltraFedFM, an innovative privacy-preserving ultrasound foundation model. UltraFedFM is collaboratively pre-trained using federated learning across 16 distributed medical institutions in 9 countries. It achieves an average area under the receiver operating characteristic curve of 0.927 for disease diagnosis and a dice similarity coefficient of 0.878 for lesion segmentation.
  arXiv  Detail & Related papers  (2024-11-25T13:40:11Z)
• Learning from the few: Fine-grained approach to pediatric wrist pathology recognition on a limited dataset [4.391219238034502]
  Wrist pathologies, particularly fractures common among children and adolescents, present a critical diagnostic challenge. Recent advancements in deep convolutional neural networks offer promise in automating pathology detection in trauma X-rays. Traditional manual annotation, though effective, is laborious, costly, and requires specialized expertise. We propose a fine-grained approach, aimed at automatically identifying discriminative regions in X-rays without manual intervention.
  arXiv  Detail & Related papers  (2024-08-24T10:14:52Z)
• Automatic Endoscopic Ultrasound Station Recognition with Limited Data [0.0]
  Pancreatic cancer is a lethal form of cancer that significantly contributes to cancer-related deaths worldwide. Despite advances in medical imaging techniques, pancreatic cancer remains a challenging disease to detect. Endoscopic ultrasound is the most effective diagnostic tool for detecting pancreatic cancer. To obtain complete imaging of the pancreas, practitioners must learn to guide the endoscope into multiple "EUS stations"
  arXiv  Detail & Related papers  (2023-09-21T06:40:05Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.