Deep Learning for the Digital Pathologic Diagnosis of Cholangiocarcinoma and Hepatocellular Carcinoma: Evaluating the Impact of a Web-based Diagnostic Assistant
| Autor: | Uyumazturk, Bora, Kiani, Amirhossein, Rajpurkar, Pranav, Wang, Alex, Ball, Robyn L., Gao, Rebecca, Yu, Yifan, Jones, Erik, Langlotz, Curtis P., Martin, Brock, Berry, Gerald J., Ozawa, Michael G., Hazard, Florette K., Brown, Ryanne A., Chen, Simon B., Wood, Mona, Allard, Libby S., Ylagan, Lourdes, Ng, Andrew Y., Shen, Jeanne |
|---|---|
| Rok vydání: | 2019 |
| Předmět: | |
| Druh dokumentu: | Working Paper |
| Popis: | While artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, the question of how best to incorporate these algorithms into clinical workflows remains relatively unexplored. We investigated how AI can affect pathologist performance on the task of differentiating between two subtypes of primary liver cancer, hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC). We developed an AI diagnostic assistant using a deep learning model and evaluated its effect on the diagnostic performance of eleven pathologists with varying levels of expertise. Our deep learning model achieved an accuracy of 0.885 on an internal validation set of 26 slides and an accuracy of 0.842 on an independent test set of 80 slides. Despite having high accuracy on a hold out test set, the diagnostic assistant did not significantly improve performance across pathologists (p-value: 0.184, OR: 1.287 (95% CI 0.886, 1.871)). Model correctness was observed to significantly bias the pathologist decisions. When the model was correct, assistance significantly improved accuracy across all pathologist experience levels and for all case difficulty levels (p-value: < 0.001, OR: 4.289 (95% CI 2.360, 7.794)). When the model was incorrect, assistance significantly decreased accuracy across all 11 pathologists and for all case difficulty levels (p-value < 0.001, OR: 0.253 (95% CI 0.126, 0.507)). Our results highlight the challenges of translating AI models to the clinical setting, especially for difficult subspecialty tasks such as tumor classification. In particular, they suggest that incorrect model predictions could strongly bias an expert's diagnosis, an important factor to consider when designing medical AI-assistance systems. Comment: Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract |
| Databáze: | arXiv |
| Externí odkaz: |
|