Artificial intelligence technology applications in the pathologic diagnosis of the gastrointestinal tract
Abstract
Artificial intelligence (AI) is a complex technology with a steady flow of new applications, including in the pathology laboratory. Applications of AI in pathology are scarce but increasing; they are based on complex software-based machine learning with deep learning trained by pathologists. Their uses are based on tissue identification on histologic slides for classification into categories of normal, nonneoplastic and neoplastic conditions. Most AI applications are based on digital pathology. This commentary describes the role of AI in the pathological diagnosis of the gastrointestinal tract and provides insights into problems and future applications by answering four fundamental questions.
Papers of special note have been highlighted as: • of interest; •• of considerable interest
References
- 1. American Medical Association. Augmented intelligence in healthcare. (2020) www.ama-assn.org/system/files/2019-01/augmented-intelligence-policy-report.pdf
- 2. . How artificial intelligence will impact colonoscopy and colorectal screening. Gastrointest. Endosc. Clin. N. Am. 30(3), 585–595 (2020).
- 3. . Accuracy of artificial intelligence-assisted detection of upper GI lesions: a systematic review and meta-analysis. Gastrointest. Endosc. S0016-5107(20), 34459–X (2020) (In Press). •• This systematic review of 23 studies highlights the accuracy of artificial intelligence (AI) in the detection of upper gastrointestinal neoplastic lesions and Heliobacter pylori infection status.
- 4. A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology. Endosc. Int. Open. 7(12), E1616–E1623 (2019).
- 5. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci. Rep. 8(1), 12054 (2018).
- 6. Artificial intelligence-based breast cancer nodal metastasis detection: insights into the black box for pathologists. Arch. Pathol. Lab. Med. 143(7), 859–868 (2019). •• This study highlights that AI algorithms can exhaustively evaluate every tissue patch on a slide, achieving comparable slide-level performance to pathologists.
- 7. Insights into pathogenic interactions among environment, host, and tumor at the crossroads of molecular pathology and epidemiology. Annu. Rev. Pathol. 14, 83–103 (2019).
- 8. . Machine learning approaches for pathologic diagnosis. Virchows Arch. 475(2), 131–138 (2019). • This study review the available and near-future types of AI.
- 9. Incidence of diagnostic change in colorectal polyp specimens after deeper sectioning at 2 different laboratories staffed by the same pathologists. Am. J. Clin. Pathol. 140(2), 231–237 (2013).
- 10. . Diagnosis and management of low-grade dysplasia in Barrett's esophagus: expert review from the Clinical Practice Updates Committee of the American Gastroenterological Association. Gastroenterology. 151(5), 822–835 (2016).
- 11. Artificial intelligence-based breast cancer nodal metastasis detection: insights into the black box for pathologists. Arch. Pathol. Lab. Med. 143(7), 859–868 (2019).
- 12. Whole slide imaging equivalency and efficiency study: experience at a large academic center. Mod. Pathol. 32(7), 916–928 (2019).
- 13. Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach. J. Pathol. Inform. 10, 7 (2019).
- 14. A deep learning framework to discern and count microscopic nematode eggs. Sci. Rep. 8(1), 9145 (2018).
- 15. A deep learning convolutional neural network can recognize common patterns of injury in gastric pathology. Arch. Pathol. Lab. Med. 144(3), 370–378 (2020).
- 16. Quantification of hepatic steatosis in histologic images by deep learning method. J. Xray. Sci. Technol. 27(6), 1033–1045 (2019).
- 17. Deep learning enables automated scoring of liver fibrosis stages. Sci. Rep. 8(1), 16016 (2018).
- 18. Deep learning for classification of colorectal polyps on whole-slide images. J. Pathol. Inform. 8, 30 (2017).
- 19. Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Sci. Rep. 10(1), 1504 (2020).
- 20. Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: a deep learning approach. Med. Image. Anal. 49, 35–45 (2018).
- 21. . Deep learning with sampling in colon cancer histology. Front. Bioeng. Biotechnol. 7, 52 (2019).
- 22. Deep learning based nucleus classification in pancreas histological images. Conf. Proc. IEEE Eng. Med. Bio. l Soc. 2017, 672–675 (2017).
- 23. . A histopathologic feature of the behavior of gastric signet-ring cell carcinoma; an image analysis study with deep learning. Pathol. Int. 69(7), 437–439 (2019).
- 24. Attention-based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides. JAMA Netw. Open. 2(11), e1914645 (2019).
- 25. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep. 8(1), 3395 (2018).
- 26. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 395(10221), 350–360 (2020).
- 27. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med. 16(1), e1002730 (2019).
- 28. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25(7), 1054–1056 (2019).
- 29. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput. Med. Imaging. Graph. 61, 2–13 (2017).
- 30. . Pathologists will prevail. Arch. Pathol. Lab. Med. 144(4), 416–419 (2020).
- 31. The pathologist workforce in the United States: II, an interactive modeling tool for analyzing future qualitative and quantitative staffing demands for services. Arch. Pathol. Lab. Med. 139(11), 1413–1430 (2015).
- 32. Gastric pathology image classification using stepwise fine-tuning for deep neural networks. J. Health Eng. 8961781 (2018).