Artificial Intelligence in Pathology: Friend or Enemy?
PDF
Cite
Share
Request
Invited Paper
P: 13-19
March 2022

Artificial Intelligence in Pathology: Friend or Enemy?

J Ankara Univ Fac Med 2022;75(1):13-19
1. Ankara Üniversitesi Tıp Fakültesi, Patoloji Anabilim Dalı, Ankara, Türkiye
2. Simplex Bilgi Teknolojileri, Ankara, Türkiye
3. Sivas Cumhuriyet Üniversitesi Tıp Fakültesi, Sivas, Türkiye
No information available.
No information available
Received Date: 11.11.2022
Accepted Date: 23.11.2022
Publish Date: 18.01.2023
PDF
Cite
Share
Request

ABSTRACT

Artificial intelligence technologies have been used frequently in many areas of life in recent years, and the frequency of its’ use in the field of medicine is increasing. In medical pathology, which is a specialty that diagnoses diseases and directs patient management, artificial intelligence-based algorithms have started to find a place in the routine practice. With the more frequent use of these technologies, the positive and negative impacts of artificial intelligence in pathology have become open to discussion. In this review, it is aimed to summarize the routine workflow in medical pathology, to examine the concept of artificial intelligence from the perspective of pathology, to evaluate the impact of artificial intelligence on pathology practice, and to assess what may await the future generations of pathologists.

Keywords: Pathology, Artificial Intelligence, Whole Slide Imaging, Region Of Interest

References

1
Turing, A. Computing machinery and intelligence (1950). https://doi.org/10.1093/oso/9780198250791.003.0017
2
Warwick K, Shah H. Passing the Turing Test Does Not Mean the End of Humanity. Cognit Comput. 2016;8:409-419.
3
Xu Y, Liu X, Cao X, et al. Artificial intelligence: A powerful paradigm for scientific research. Innovation (Camb). 2021;2:100179.
4
Chang HY, Jung CK, Woo JI, et al. Artificial Intelligence in Pathology. J Pathol Transl Med. 2019;53:1-12.
5
Shortliffe EH. Mycin: A Knowledge Based Computer Program Applied to Infectious Diseases. Proceedings of the Annual Symposium on Computer Application in Medical Care. 1977;66-69.
6
Heckerman DE, Nathwani BN. An evaluation of the diagnostic accuracy of Pathfinder. Comput Biomed Res. 1992;25:56-74.
7
Ozkan TA, Eruyar AT, Cebeci OO, et al. Interobserver variability in Gleason histological grading of prostate cancer. Scand J Urol. 2016;50:420-424.
8
Bulten W, Pinckaers H, van Boven H, et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 2020;21:233-241.
9
Munir K, Elahi H, Ayub A, et al. Cancer Diagnosis Using Deep Learning: A Bibliographic Review. Cancers (Basel). 2019;11:1235.
10
Eelbode T, Bertels J, Berman M, et al. Optimization for Medical Image Segmentation: Theory and Practice When Evaluating With Dice Score or Jaccard Index. IEEE Trans Med Imaging. 2020;39:3679-3690.
11
Hanna MG, Parwani A, Sirintrapun SJ. Whole Slide Imaging: Technology and Applications. Adv Anat Pathol. 2020;27:251-259.
12
Wang M, Aung PP, Prieto VG. Standardized Method for Defining a 1-mm2 Region of Interest for Calculation of Mitotic Rate on Melanoma Whole Slide Images. Arch Pathol Lab Med. 2021;145:1255-1263.
13
Komura D, Ishikawa S. Machine learning approaches for pathologic diagnosis. Virchows Arch. 2019;475:131-138.
14
Roohi A, Faust K, Djuric U, Diamandis P. Unsupervised Machine Learning in Pathology: The Next Frontier. Surg Pathol Clin. 2020;13:349-358.
15
Wang S, Yang DM, Rong R, et al. Pathology Image Analysis Using Segmentation Deep Learning Algorithms. Am J Pathol. 2019;189:1686-1698.
16
Jiang Y, Yang M, Wang S, et al. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020;40:154-166.
17
Lee K, Lockhart JH, Xie M, et al. Deep Learning of Histopathology Images at the Single Cell Level. Front Artif Intell. 2021;4:754641.
18
Tufail AB, Ma YK, Kaabar MKA, et al. Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. Comput Math Methods Med. 2021;2021:9025470.
19
Yousif M, van Diest PJ, Laurinavicius A, et al. Artificial intelligence applied to breast pathology. Virchows Arch. 2022;480:191-209.
20
Sato N, Uchino E, Kojima R, et al. Evaluation of Kidney Histological Images Using Unsupervised Deep Learning. Kidney Int Rep. 2021;6:2445-2454.
21
Jin L, Shi F, Chun Q, et al. Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers. Neuro Oncol. 2021;23:44-52.
22
Tao Y, Huang X, Tan Y, et al. Qualitative Histopathological Classification of Primary Bone Tumors Using Deep Learning: A Pilot Study. Front Oncol. 2021;11:735739.
23
Brück OE, Lallukka-Brück SE, Hohtari HR, et al. Machine Learning of Bone Marrow Histopathology Identifies Genetic and Clinical Determinants in Patients with MDS. Blood Cancer Discov. 2021;2:238-249.
24
Hong R, Liu W, DeLair D, et al. Fenyö D. Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models. Cell Rep Med. 2021;2:100400.
25
Wang X, Zou C, Zhang Y, et al. Prediction of BRCA Gene Mutation in Breast Cancer Based on Deep Learning and Histopathology Images. Front Genet. 2021;12:661109.
26
Qu H, Zhou M, Yan Z, et al. Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning. NPJ Precis Oncol. 2021;5:87.
27
Hammouda K, Khalifa F, El-Melegy M, et al. A Deep Learning Pipeline for Grade Groups Classification Using Digitized Prostate Biopsy Specimens. Sensors (Basel). 2021;21:6708.
28
Li J, Garfinkel J, Zhang X, et al. Biopsy-free in vivo virtual histology of skin using deep learning. Light Sci Appl. 2021;10:233.
29
Schaumberg AJ, Juarez-Nicanor WC, Choudhury SJ, et al. Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media. Mod Pathol. 2020;33:2169-2185.
30
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. 2019;143:859-868.
31
Kim H, Yoon H, Thakur N, et al. Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain. Sci Rep. 2021;11:22520.
32
Sevim, S, Serbes ED, Ozdogan, G, et al. (2022). Eyeballing and hot-spot counting of Ki67 may misguide therapy in invasive breast carcinoma, NST and the quick fix is automated counting. Oral Paper Presentation, 34th European Congress of Pathology, Basel.
33
Sensu S, Erdogan N, Gurbuz YS. (2020). Patolojide Dijital Çağ ve Yapay Zekâ: Temel Bilgiler Digital Era and Artificial Intelligence in Pathology: Basic Information. Turkish Journal of Medical Sciences. 2010;40:104-112.
34
Carter SM, Rogers W, Win KT, et al. The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. Breast. 2020;49:25-32.
35
Ryan M. In AI We Trust: Ethics, Artificial Intelligence, and Reliability. Sci Eng Ethics. 2020;26:2749-2767.
36
Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019;20:e253-e261.
37
Cangir AK, Orhan K, Kahya Y, et al. A CT-Based Radiomic Signature for the Differentiation of Pulmonary Hamartomas from Carcinoid Tumors. Diagnostics (Basel). 2022;12:416.
2024 ©️ Galenos Publishing House