2024-10-24 ミュンヘン大学(LMU)
<関連情報>
- https://www.lmu.de/en/newsroom/news-overview/news/ai-in-medicine-new-approach-for-more-efficient-diagnostics.html
- https://ai.nejm.org/doi/full/10.1056/AIoa2400468
臨床レベルの病理組織診断のためのAIベースの異常検出 AI-Based Anomaly Detection for Clinical-Grade Histopathological Diagnostics
Jonas Dippel, M.Sc., Niklas Prenißl, M.D., Julius Hense, M.Sc., Philipp Liznerski, M.Sc., Tobias Winterhoff, M.Sc., Simon Schallenberg, M.D., Marius Kloft, Ph.D., +5, and Frederick Klauschen, M.D.
NEJM AI Published: October 18, 2024
DOI: 10.1056/AIoa2400468
Abstract
Background
While previous studies of artificial intelligence (AI) have shown its potential for diagnosing diseases using imaging data, clinical implementation lags behind. AI models require training with large numbers of examples, which are only available for common diseases. In clinical reality, however, the majority of diseases are less frequent, and current AI models overlook or misclassify them. An effective, comprehensive technique is needed for the full spectrum of real-world diagnoses.
Methods
We collected two large real-world datasets of gastrointestinal (GI) biopsies, which are prototypical of the problem. Herein, the 10 most common findings accounted for approximately 90% of cases, whereas the remaining 10% contained 56 disease entities, including many cancers. Seventeen million histological images from 5423 cases were used for training and evaluation. We propose a deep anomaly detection (AD) approach that only requires training data from common diseases to also detect all less frequent diseases.
Results
Without specific training for the diseases, our best-performing model reliably detected a broad spectrum of infrequent (“anomalous”) pathologies with 95.0% (stomach) and 91.0% (colon) area under the receiver operating characteristic curve (AUROC) and was able to generalize between scanners and hospitals. Cancers were detected with 97.7% (stomach) and 96.9% (colon) AUROC. Heatmaps reliably highlighted anomalous areas and can guide pathologists during the diagnostic process.
Conclusions
In this study, we establish the first effective clinical application of AI-based AD in histopathology and demonstrate high performance on a unique real-world collection of GI biopsies. The proposed novel AD can flag anomalous cases, facilitate case prioritization, and reduce missed diagnoses, providing critical support for pathologists. By design, it can be expected to detect any pathological alteration including rare primary or metastatic cancers in GI biopsies. To our knowledge, no other published AI tool is capable of zero-shot pan-cancer detection. AD may enhance the safety of AI models in histopathology, thereby driving AI adoption and automation in routine diagnostics and beyond.
