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<p>Odontogenic Keratocyst (OKC) is a benign jaw tumor characterized by a high recurrence rate. However, its clinical diagnosis presents significant challenges due to complex pathological morphologies and inconspicuous early symptoms. In this paper, we propose an intelligent OKC diagnosis system that leverages multimodal data fusion and interpretable analysis. Specifically, we first devise a Multimodal Feature-based Diagnosis Model (MFDM), which utilizes a Spatial Feature Fusion Module (SFFM) to fuse features extracted from patients’ oral pathological slides and tabular clinical parameters. Second, we formulate an Interpretable Recurrence Prediction Model (IRPM) that extracts features from patient demographics and medical history records. This model harnesses an attention mechanism to weight these features, thereby realizing a quantitative estimation of OKC recurrence risk. Furthermore, we implement an integrated information management platform that deploys both MFDM and IRPM. This platform facilitates OKC detection, risk prediction, case storage, and key data visualization, significantly facilitating the algorithm’s usability. We conduct extensive comparative and ablation experiments on collected datasets. The results demonstrate that our approach achieves higher accuracy in both diagnosis and recurrence prediction compared to existing state-of-the-art models. Moreover, the visualization elucidates the rationale behind the model’s decision-making process, reinforcing its interpretability and credibility for clinical applications.</p>

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A deep learning approach for the diagnosis and recurrence prediction of OKC

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月23日

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  • Weihua Chen,
  • Maoming Qian,
  • Mingbo Zhang,
  • Wende Yang,
  • Ruipeng Gao,
  • Jianyun Zhang,
  • Chaoran Peng,
  • Xinjia Cai

摘要

Odontogenic Keratocyst (OKC) is a benign jaw tumor characterized by a high recurrence rate. However, its clinical diagnosis presents significant challenges due to complex pathological morphologies and inconspicuous early symptoms. In this paper, we propose an intelligent OKC diagnosis system that leverages multimodal data fusion and interpretable analysis. Specifically, we first devise a Multimodal Feature-based Diagnosis Model (MFDM), which utilizes a Spatial Feature Fusion Module (SFFM) to fuse features extracted from patients’ oral pathological slides and tabular clinical parameters. Second, we formulate an Interpretable Recurrence Prediction Model (IRPM) that extracts features from patient demographics and medical history records. This model harnesses an attention mechanism to weight these features, thereby realizing a quantitative estimation of OKC recurrence risk. Furthermore, we implement an integrated information management platform that deploys both MFDM and IRPM. This platform facilitates OKC detection, risk prediction, case storage, and key data visualization, significantly facilitating the algorithm’s usability. We conduct extensive comparative and ablation experiments on collected datasets. The results demonstrate that our approach achieves higher accuracy in both diagnosis and recurrence prediction compared to existing state-of-the-art models. Moreover, the visualization elucidates the rationale behind the model’s decision-making process, reinforcing its interpretability and credibility for clinical applications.