A Unified Multimodal Framework for Dataset Construction and Model-Based Diagnosis of Ameloblastoma
This work addresses the problem of limited diagnostic resources for maxillofacial pathologists by providing an incremental dataset and model for ameloblastoma, a specific domain in medical AI.
The authors tackled the lack of structured multimodal datasets for ameloblastoma diagnosis by curating a new dataset integrating radiological, histopathological, and clinical data, and developed a multimodal deep learning model that improved variant classification accuracy from 46.2% to 65.9% and abnormal tissue detection F1-score from 43.0% to 90.3%.
Artificial intelligence (AI)-enabled diagnostics in maxillofacial pathology require structured, high-quality multimodal datasets. However, existing resources provide limited ameloblastoma coverage and lack the format consistency needed for direct model training. We present a newly curated multimodal dataset specifically focused on ameloblastoma, integrating annotated radiological, histopathological, and intraoral clinical images with structured data derived from case reports. Natural language processing techniques were employed to extract clinically relevant features from textual reports, while image data underwent domain specific preprocessing and augmentation. Using this dataset, a multimodal deep learning model was developed to classify ameloblastoma variants, assess behavioral patterns such as recurrence risk, and support surgical planning. The model is designed to accept clinical inputs such as presenting complaint, age, and gender during deployment to enhance personalized inference. Quantitative evaluation demonstrated substantial improvements; variant classification accuracy increased from 46.2 percent to 65.9 percent, and abnormal tissue detection F1-score improved from 43.0 percent to 90.3 percent. Benchmarked against resources like MultiCaRe, this work advances patient-specific decision support by providing both a robust dataset and an adaptable multimodal AI framework.