Artificial intelligence technology applications in the pathologic diagnosis of the gastrointestinal tract

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
  Google Scholar
• 2. Shung DL, Byrne MF. How artificial intelligence will impact colonoscopy and colorectal screening. Gastrointest. Endosc. Clin. N. Am. 30(3), 585–595 (2020).
  MedlineGoogle Scholar
• 3. Lui TK, Tsui VW, Leung WK. 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.
  Google Scholar
• 4. Ebigbo A, Palm C, Probst A et al. A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology. Endosc. Int. Open. 7(12), E1616–E1623 (2019).
  MedlineGoogle Scholar
• 5. Arvaniti E, Fricker KS, Moret M et al. Automated Gleason grading of prostate cancer tissue microarrays via deep learning. Sci. Rep. 8(1), 12054 (2018).
  MedlineGoogle Scholar
• 6. Liu Y, Kohlberger T, Norouzi M et al. 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.
  Medline CASGoogle Scholar
• 7. Ogino S, Nowak JA, Hamada T et al. Insights into pathogenic interactions among environment, host, and tumor at the crossroads of molecular pathology and epidemiology. Annu. Rev. Pathol. 14, 83–103 (2019).
  Medline CASGoogle Scholar
• 8. Komura D, Ishikawa S. Machine learning approaches for pathologic diagnosis. Virchows Arch. 475(2), 131–138 (2019). • This study review the available and near-future types of AI.
  Medline CASGoogle Scholar
• 9. Nielsen JA, Lager DJ, Lewin M et al. 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).
  MedlineGoogle Scholar
• 10. Wani S, Rubenstein JH, Vieth M, Bergman J. 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).
  MedlineGoogle Scholar
• 11. Liu Y, Kohlberger T, Norouzi M et al. Artificial intelligence-based breast cancer nodal metastasis detection: insights into the black box for pathologists. Arch. Pathol. Lab. Med. 143(7), 859–868 (2019).
  Medline CASGoogle Scholar
• 12. Hanna MG, Reuter VE, Hameed MR et al. Whole slide imaging equivalency and efficiency study: experience at a large academic center. Mod. Pathol. 32(7), 916–928 (2019).
  MedlineGoogle Scholar
• 13. Wei JW, Wei JW, Jackson CR et al. Automated detection of celiac disease on duodenal biopsy slides: a deep learning approach. J. Pathol. Inform. 10, 7 (2019).
  Medline CASGoogle Scholar
• 14. Akintayo A, Tylka GL, Singh AK et al. A deep learning framework to discern and count microscopic nematode eggs. Sci. Rep. 8(1), 9145 (2018).
  MedlineGoogle Scholar
• 15. Martin DR, Hanson JA, Gullapalli RR et al. A deep learning convolutional neural network can recognize common patterns of injury in gastric pathology. Arch. Pathol. Lab. Med. 144(3), 370–378 (2020).
  MedlineGoogle Scholar
• 16. Yang F, Jia X, Lei P et al. Quantification of hepatic steatosis in histologic images by deep learning method. J. Xray. Sci. Technol. 27(6), 1033–1045 (2019).
  MedlineGoogle Scholar
• 17. Yu Y, Wang J, Ng CW et al. Deep learning enables automated scoring of liver fibrosis stages. Sci. Rep. 8(1), 16016 (2018).
  MedlineGoogle Scholar
• 18. Korbar B, Olofson AM, Miraflor AP et al. Deep learning for classification of colorectal polyps on whole-slide images. J. Pathol. Inform. 8, 30 (2017).
  MedlineGoogle Scholar
• 19. Iizuka O, Kanavati F, Kato K et al. Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Sci. Rep. 10(1), 1504 (2020).
  Medline CASGoogle Scholar
• 20. Van Eycke YR, Balsat C, Verset L et al. Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: a deep learning approach. Med. Image. Anal. 49, 35–45 (2018).
  MedlineGoogle Scholar
• 21. Shapcott M, Hewitt KJ, Rajpoot N. Deep learning with sampling in colon cancer histology. Front. Bioeng. Biotechnol. 7, 52 (2019).
  MedlineGoogle Scholar
• 22. Chang Young Hwan, Thibault G, Madin O et al. Deep learning based nucleus classification in pancreas histological images. Conf. Proc. IEEE Eng. Med. Bio. l Soc. 2017, 672–675 (2017).
  MedlineGoogle Scholar
• 23. Mori H, Miwa H. 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).
  MedlineGoogle Scholar
• 24. Tomita N, Abdollahi B, Wei J et al. Attention-based deep neural networks for detection of cancerous and precancerous esophagus tissue on histopathological slides. JAMA Netw. Open. 2(11), e1914645 (2019).
  MedlineGoogle Scholar
• 25. Bychkov D, Linder N, Turkki R et al. Deep learning based tissue analysis predicts outcome in colorectal cancer. Sci. Rep. 8(1), 3395 (2018).
  MedlineGoogle Scholar
• 26. Skrede OJ, De Raedt S, Kleppe A et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 395(10221), 350–360 (2020).
  Medline CASGoogle Scholar
• 27. Kather JN, Krisam J, Charoentong P et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med. 16(1), e1002730 (2019).
  MedlineGoogle Scholar
• 28. Kather JN, Pearson AT, Halama N et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25(7), 1054–1056 (2019).
  Medline CASGoogle Scholar
• 29. Sharma H, Zerbe N, Klempert I et al. 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).
  MedlineGoogle Scholar
• 30. Allen TC. Pathologists will prevail. Arch. Pathol. Lab. Med. 144(4), 416–419 (2020).
  MedlineGoogle Scholar
• 31. Robboy SJ, Gupta S, Crawford JM et al. 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).
  MedlineGoogle Scholar
• 32. Qu J, Hiruta N, Terai K, Nosato H et al. Gastric pathology image classification using stepwise fine-tuning for deep neural networks. J. Health Eng. 8961781 (2018).
  MedlineGoogle Scholar