دورية أكاديمية
AI-based prostate analysis system trained without human supervision to predict patient outcome from tissue samples
| العنوان: | AI-based prostate analysis system trained without human supervision to predict patient outcome from tissue samples |
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| المؤلفون: | Peter Walhagen, Ewert Bengtsson, Maximilian Lennartz, Guido Sauter, Christer Busch |
| المصدر: | Journal of Pathology Informatics, Vol 13, Iss , Pp 100137- (2022) |
| بيانات النشر: | Elsevier, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics LCC:Pathology |
| مصطلحات موضوعية: | Prostate cancer grading, Artificial intelligence-based cancer grading, Predicting prostate cancer recurrence, Computer applications to medicine. Medical informatics, R858-859.7, Pathology, RB1-214 |
| الوصف: | In order to plan the best treatment for prostate cancer patients, the aggressiveness of the tumor is graded based on visual assessment of tissue biopsies according to the Gleason scale. Recently, a number of AI models have been developed that can be trained to do this grading as well as human pathologists. But the accuracy of the AI grading will be limited by the accuracy of the subjective “ground truth” Gleason grades used for the training. We have trained an AI to predict patient outcome directly based on image analysis of a large biobank of tissue samples with known outcome without input of any human knowledge about cancer grading. The model has shown similar and in some cases better ability to predict patient outcome on an independent test-set than expert pathologists doing the conventional grading. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2153-3539 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2153353922007313; https://doaj.org/toc/2153-3539 |
| DOI: | 10.1016/j.jpi.2022.100137 |
| URL الوصول: | https://doaj.org/article/de36a3db03bb4e569d3b3503b403b2ef |
| رقم الأكسشن: | edsdoj.36a3db03bb4e569d3b3503b403b2ef |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text via ClinicalKey 
Full Text Finder | تدمد: | 21533539 |
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| DOI: | 10.1016/j.jpi.2022.100137 |