Искусственный интеллект в патологической анатомии

Закрыть метаданные

Рекомендуем статьи по данной теме:

Цифровое здравоохранение Российской Федерации: основные понятия для практикующих врачей. Профилактическая медицина. 2024;(1):97-105

Сравнение прогностических возможностей традиционных методов оценки сердечно-сосудистого риска с использованием шкал SCORE и FRAMINGHAM, технологий машинного обучения «ИНТЕРЭПИД». Профилактическая медицина. 2024;(2):96-102

Первый опыт использования технологии искусственного интеллекта в системе CAD EYE при колоноскопии у детей. (Серия клинических случаев). Доказательная гастроэнтерология. 2024;(1):94-100

Разработка и тестирование новых методических подходов прогнозирования сердечно-сосудистых событий у здоровых людей с использованием технологии машинного обучения на базе международного исследования «Интерэпид». Профилактическая медицина. 2024;(3):72-79

Искусственные интеллектуальные системы в развитии вспомогательных репродуктивных технологий. Российский вестник акушера-гинеколога. 2024;(2):19-29

Роль телемедицины в дерматологии с примерами по организации услуг пациентам с псориазом и акне. Клиническая дерматология и венерология. 2024;(2):111-117

Точность приложения на основе искусственного интеллекта при выявлении хронических заболеваний вен классов C1 и C2. Флебология. 2024;(2):132-138

Влияние технологий искусственного интеллекта на длительность проведения двойного чтения маммографических исследований. Профилактическая медицина. 2024;(5):32-37

В обзоре представлены ключевые концепции и мировые разработки в области искусственного интеллекта (ИИ), применяемые в патологической анатомии. В работе рассматривается два типа ИИ: слабый и сильный. Проведен обзор экспериментальных алгоритмов, использующих как глубокое машинное обучение, так и технологии компьютерного зрения для работы с WSI-изображениями препаратов, диагностики и определения прогноза различных злокачественных новообразований. Установлено, что слабый ИИ на данном этапе развития компьютерной (цифровой) патологической анатомии значительно лучше ускоряет и уточняет диагностические процедуры, нежели сильный ИИ, обладающий признаками общего интеллекта. В статье также рассматриваются три варианта дальнейшего развития ИИ-помощников врача-патологоанатома на базе технологий больших языковых моделей ChatGPT (PathAsst), Flan-PaLM2 и LIMA. В результате анализа литературы выявлены ключевые проблемы области: оснащенность учреждений, недостаток экспертов по обучению нейросетей, отсутствие строгих критериев клинической состоятельности технологий ИИ-диагностики.

Ключевые слова:

анализ WSI

патологическая анатомия

ИИ-помощник патологоанатома

глубокое машинное обучение

нейронные сети

искусственный интеллект

Авторы:

Соловьев И.А.

ФГБОУ ВО «Сыктывкарский государственный университет им. Питирима Сорокина»

ORCID: 0000-0002-1038-2271

Дата поступления:

30.10.2023

Дата принятия в печать:

27.12.2023

Список литературы:

Al-Rifaie MM, Bishop M. Weak and strong computational creativity. Ch. In: Besold T, Schorlemmer M, Smaill A, eds. Computational creativity research: towards creative machines. Atlantis Thinking Machines. Vol. 7. Paris: Atlantis Press; 2014;37-49. https://doi.org/10.2991/978-94-6239-085-0_2
Meyer J, Khademi A, Têtu B, Han W, Nippak P, Remisch D. Impact of artificial intelligence on pathologists’ decisions: an experiment. J Am Med Inform Assoc. 2022;29(10):1688-1695. https://doi.org/10.1093/jamia/ocac103
Nakagawa K, Moukheiber L, Celi LA, Patel M, Mahmood F, Gondim D, Hogarth M, Levenson R. AI in pathology: what could possibly go wrong? Semin Diagn Pathol. 2023;40(2):100-108. https://doi.org/10.1053/j.semdp.2023.02.006
Försch S, Klauschen F, Hufnagl P, Roth W. Artificial intelligence in pathology. Dtsch Arztebl Int. 2021;118(12):194-204. https://doi.org/10.3238/arztebl.m2021.0011
Drogt J, Milota M, Vos S, Bredenoord A, Jongsma K. Integrating artificial intelligence in pathology: a qualitative interview study of users’ experiences and expectations. Mod Pathol. 2022;35(11): 1540-1550. https://doi.org/10.1038/s41379-022-01123-6
Cooper MC, Ji Z, Krishnan RG. Machine learning in computational histopathology: challenges and opportunities. Genes Chromosomes Cancer. 2023;62(9):540-556. https://doi.org/10.1002/gcc.23177
Cifci D, Veldhuizen G, Foersch S, Kather JN. AI in computational pathology of cancer: improving diagnostic workflows and clinical outcomes? Ann Rev Cancer Biol. 2023;7(1):57-71. https://doi.org/10.1146/annurev-cancerbio-061521-092038
Wang S, Yang DM, Rong R, Zhan X, Xiao G. Pathology image analysis using segmentation deep learning algorithms. Am J Pathol. 2019;189(9):1686-1698. https://doi.org/10.1016/j.ajpath.2019.05.007
Ghaffari Laleh N, Truhn D, Veldhuizen GP, Han T, van Treeck M, Buelow RD, Langer R, Dislich B, Boor P, Schulz V, et al. Adversarial attacks and adversarial robustness in computational pathology. Nat Commun. 2022;13(1):5711. https://doi.org/10.1038/s41467-022-33266-0
Kim I, Kang K, Song Y, Kim TJ. Application of artificial intelligence in pathology: trends and challenges. Diagnostics. 2022; 12(11):2794. https://doi.org/10.3390/diagnostics12112794
Zhang Z, Chen P, McGough M, Xing F, Wang C, Bui M, Xie Y, Sapkota M, Cui L, Dhillon J, et al. Pathologist-level interpretable whole-slide cancer diagnosis with deep learning. Nat Mach Intell. 2019;1:236-245. https://doi.org/10.1038/s42256-019-0052-1
Khoraminia F, Fuster S, Kanwal N, Olislagers M, Engan K, van Leenders GJLH, Stubbs AP, Akram F, Zuiverloon TCM. Artificial intelligence in digital pathology for bladder cancer: hype or hope? A systematic review. Cancers (Basel). 2023;15(18):4518. https://doi.org/10.3390/cancers15184518
Froń A, Semianiuk A, Lazuk U, Ptaszkowski K, Siennicka A, Lemiński A, Krajewski W, Szydełko T, Małkiewicz B. Artificial intelligence in urooncology: what we have and what we expect. Cancers (Basel). 2023;15(17):4282. https://doi.org/10.3390/cancers15174282
Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol. 2021;35(1):23-32. https://doi.org/10.1038/s41379-021-00919-2
Jain DK, Lakshmi KM, Varma KP, Ramachandran M, Bharati S. Lung cancer detection based on Kernel PCA-convolution neural network feature extraction and classification by fast deep belief neural network in disease management using multimedia data sources. Comput Intell Neurosci. 2022;2022:3149406. https://doi.org/10.1155/2022/3149406
Tsuneki M, Kanavati F. Weakly supervised learning for multi-organ adenocarcinoma classification in whole slide images. PLoS One. 2022;17(11):e0275378. https://doi.org/10.1371/journal.pone.0275378
Civit-Masot J, Bañuls-Beaterio A, Domínguez-Morales M, Rivas-Pérez M, Muñoz-Saavedra L, Rodríguez Corral JM. Non-small cell lung cancer diagnosis aid with histopathological images using Explainable Deep Learning techniques. Comput Methods Programs Biomed. 2022;226:107108. https://doi.org/10.1016/j.cmpb.2022.107108
Shvetsov N, Grønnesby M, Pedersen E, Møllersen K, Busund LR, Schwienbacher R, Bongo LA, Kilvaer TK. A pragmatic machine learning approach to quantify tumor-infiltrating lymphocytes in whole slide images. Cancers (Basel). 2022;14(12):2974. https://doi.org/10.3390/cancers14122974
Qaiser T, Mukherjee A, Reddy Pb C, Munugoti SD, Tallam V, Pitkäaho T, Lehtimäki T, Naughton T, Berseth M, Pedraza A, et al. HER2 challenge contest: a detailed assessment of automated HER2 scoring algorithms in whole slide images of breast cancer tissues. Histopathology. 2018;72(2):227-238. https://doi.org/10.1111/his.13333
Cruz-Roa A, Gilmore H, Basavanhally A, Feldman M, Ganesan S, Shih NNC, Tomaszewski J, González FA, Madabhushi A. Accurate and reproducible invasive breast cancer detection in whole-slide images: a Deep Learning approach for quantifying tumor extent. Sci Rep. 2017;7:46450. https://doi.org/10.1038/srep46450
Im S, Hyeon J, Rha E, Lee J, Choi HJ, Jung Y, Kim TJ. Classification of diffuse glioma subtype from clinical-grade pathological images using deep transfer learning. Sensors (Basel). 2021;21(10):3500. https://doi.org/10.3390/s21103500
Pei L, Jones KA, Shboul ZA, Chen JY, Iftekharuddin KM. Deep neural network analysis of pathology images with integrated molecular data for enhanced glioma classification and grading. Front Oncol. 2021;11:668694. https://doi.org/10.3389/fonc.2021.668694
Khalsa SSS, Hollon TC, Adapa A, Urias E, Srinivasan S, Jairath N, Szczepanski J, Ouillette P, Camelo-Piragua S, Orringer DA. Automated histologic diagnosis of CNS tumors with machine learning. CNS Oncol. 2020;9(2):CNS56. https://doi.org/10.2217/cns-2020-0003
Zhou C, Jin Y, Chen Y, Huang S, Huang R, Wang Y, Zhao Y, Chen Y, Guo L, Liao J. Histopathology classification and localization of colorectal cancer using global labels by weakly supervised deep learning. Comput Med Imaging Graph. 2021;88:101861. https://doi.org/10.1016/j.compmedimag.2021.101861
Wang KS, Yu G, Xu C, Meng XH, Zhou J, Zheng C, Deng Z, Shang L, Liu R, Su S, et al. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Med. 2021;19(1):76. https://doi.org/10.1186/s12916-021-01942-5
Dabass M, Vashisth S, Vig R. A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images. Comput Biol Med. 2022;147:105680. https://doi.org/10.1016/j.compbiomed.2022.105680
Ba W, Wang S, Shang M, Zhang Z, Wu H, Yu C, Xing R, Wang W, Wang L, Liu C, et al. Assessment of deep learning assistance for the pathological diagnosis of gastric cancer. Mod Pathol. 2022; 35(9):1262-1268. https://doi.org/10.1038/s41379-022-01073-z
Wang SZ, Wang JG, Lu Y, Zhang YJ, Xin FJ, Liu SL, Zhang XX, Liu GW, Li S, Sui D, Clinical application of convolutional neural network in pathological diagnosis of metastatic lymph nodes of gastric cancer. Zhonghua Wai Ke Za Zhi. 2019;57(12):934-938. https://doi.org/10.3760/cma.j.issn.0529-5815.2019.12.012
Bertsimas D, Margonis GA, Tang S, Koulouras A, Antonescu CR, Brennan MF, Martin-Broto J, Rutkowski P, Stasinos G, Wang J, et al. An interpretable AI model for recurrence prediction after surgery in gastrointestinal stromal tumour: an observational cohort study. eClinicalMedicine. 2023;64:102200. https://doi.org/10.1016/j.eclinm.2023.102200
Shen M, Zou Z, Bao, Fairley CK, Canfell K, Ong JJ, Hocking J, Chow EPF, Zhuang G, Wang L, et al. Cost-effectiveness of artificial intelligence-assisted liquid-based cytology testing for cervical cancer screening in China. Lancet Reg Health West Pac. 2023;34:100726. https://doi.org/10.1016/j.lanwpc.2023.100726
Shiga T, Taguchi A, Mori M, Yamaguchi S, Honjoh H, Nishijima A, Eguchi S, Miyamoto Y, Sone K, Kawana K, et al. Risk stratification of invasive cervical cancer diagnosed after cervical conization. Jpn J Clin Oncol. 2023;53(12):1138-1143. https://doi.org/10.1093/jjco/hyad121
Alsalatie M, Alquran H, Mustafa WA, Zyout A, Alqudah AM, Kaifi R, Qudsieh S. A new weighted deep learning feature using particle swarm and ant lion optimization for cervical cancer diagnosis on pap smear images. Diagnostics (Basel). 2023;13(17):2762. https://doi.org/10.3390/diagnostics13172762
Yu H, Luo S, Ji J, Wang Z, Zhi W, Mo N, Zhong P, He C, Wan T, Jin Y. A deep-learning-based artificial intelligence system for the pathology diagnosis of uterine smooth muscle tumor. Life (Basel). 2022;13(1):3. https://doi.org/10.3390/life13010003
Deng C, Li D, Feng M, Han D, Huang Q. The value of deep neural networks in the pathological classification of thyroid tumors. Diagn Pathol. 2023;18(1):95. https://doi.org/10.1186/s13000-023-01380-2
Nagendra L, Pappachan JM, Fernandez CJ. Artificial intelligence in the diagnosis of thyroid cancer: recent advances and future directions. Artif Intell Cancer. 2023;4(1):1-10. https://doi.org/10.35713/aic.v4.i1.1
Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: a comprehensive review. Comput Biol Med. 2022;146:105580. https://doi.org/10.1016/j.compbiomed.2022.105580
Busby D, Grauer R, Pandav K, Khosla A, Jain P, Menon M, Haines GK 3rd, Cordon-Cardo C, Gorin MA, Tewari AK. Applications of artificial intelligence in prostate cancer histopathology. Urol Oncol. 2023;S1078-1439(22)00487-2. https://doi.org/10.1016/j.urolonc.2022.12.002
Boellaard R, Wesseling D, Eertink C, de Vries B, Lugtenburg E, Zwezerijnen B, Wiegers S, Vet R, Golla S, Zijlstra J. Artificial Intelligence based outcome classification from baseline F-18-FDG PET/CT in de novo diffuse Large B-cell lymphoma patients. Eur J Nucl Med Mol Imaging. 2021;48(suppl 1):S348-S348.
Li D, Bledsoe JR, Zeng Y, Li D, Bledsoe JR, Zeng Y, Liu W, Hu Y, Bi K, Liang A, et al. A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals. Nat Commun. 2020;11(1):6004. https://doi.org/10.1038/s41467-020-19817-3
Steinbuss G, Kriegsmann M, Zgorzelski C, Brobeil A, Goeppert B, Dietrich S, Mechtersheimer G, Kriegsmann K. Deep learning for the classification of Non-Hodgkin lymphoma on histopathological images. Cancers (Basel). 2021;13(10):2419. https://doi.org/10.3390/cancers13102419
Irshaid L, Bleiberg J, Weinberger E, Garritano J, Shallis RM, Patsenker J, Lindenbaum O, Kluger Y, Katz SG, Xu ML. Histopathologic and machine deep learning criteria to predict lymphoma transformation in bone marrow biopsies. Arch Pathol Lab Med. 2022;146(2):182-193. https://doi.org/10.5858/arpa.2020-0510-OA
Miyoshi H, Sato K, Kabeya Y, Yonezawa S, Nakano H, Takeuchi Y, Ozawa I, Higo S, Yanagida E, Yamada K, et al. Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma. Lab Invest. 2020;100(10):1300-1310. https://doi.org/10.1038/s41374-020-0442-3
Achi HE, Belousova T, Chen L, Wahed A, Wang I, Hu Z, Kanaan Z, Rios A, Nguyen AND. Automated diagnosis of ymphoma with digital pathology images using deep learning. Ann Clin Lab Sci. 2019;49(2):153-160.
Mallesh N, Zhao M, Meintker L, Höllein A, Elsner F, Lüling H, Haferlach T, Kern W, Westermann J, Brossart P, et al. Knowledge transfer to enhance the performance of deep learning models for automated classification of B cell neoplasms. Patterns (N Y). 2021;2(10):100351. https://doi.org/10.1016/j.patter.2021.100351
Doeleman T, Hondelink LM, Vermeer MH, van Dijk MR, Schrader AMR. Artificial intelligence in digital pathology of cutaneous lymphomas: a review of the current state and future perspectives. Semin Cancer Biol. 2023;94:81-88. https://doi.org/10.1016/j.semcancer.2023.06.004
Khan A, Brouwer N, Blank A, Müller F, Soldini D, Noske A, Gaus E, Brandt S, Nagtegaal I, Dawson H, et al. Computer-assisted diagnosis of lymph node metastases in colorectal cancers using transfer learning with an ensemble model. Mod Pathol. 2023; 36(5):100118. https://doi.org/10.1016/j.modpat.2023.100118
Kiehl L, Kuntz S, Höhn J, Jutzi T, Krieghoff-Henning E, Kather JN, Holland-Letz T, Kopp-Schneider A, Chang-Claude J, Brobeil A, et al. Deep learning can predict lymph node status directly from histology in colorectal cancer. Eur J Cancer. 2021;157:464-473. https://doi.org/10.1016/j.ejca.2021.08.039
Ehteshami Bejnordi B, Veta M, Johannes van Diest P, van Ginneken B, Karssemeijer N, Litjens G, van der Laak JAWM; the CAMELYON16 Consortium; et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017;318(22):2199. https://doi.org/10.1001/jama.2017.14585
Caldonazzi N, Rizzo PC, Eccher A, Girolami I, Fanelli GN, Naccarato AG, Bonizzi G, Fusco N, d’Amati G, Scarpa A, et al. Value of artificial intelligence in evaluating lymph node metastases. Cancers (Basel). 2023;15(9):2491. https://doi.org/10.3390/cancers15092491
Sounderajah V, Ashrafian H, Golub RM, Shetty S, De Fauw J, Hooft L, Moons K, Collins G, Moher D, Bossuyt PM, et al.; STARD-AI Steering Committee. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open. 2021;11(6):e047709. https://doi.org/10.1136/bmjopen-2020-047709
Giarnieri E, Scardapane S. Towards artificial intelligence applications in next generation cytopathology. Biomedicines. 2023; 11(8):2225. https://doi.org/10.3390/biomedicines11082225
Chowdhery A, Narang S, Devlin J, Bosma M, Mishra G, Roberts A, Barham P, Chung HW, Sutton C, Gehrmann S, et al. PaLM: scaling language modeling with pathways. arXiv:220402311. 2022. https://arxiv.org/abs/2204.02311
Nori H, King N, McKinney SM, Carignan D, Horvitz E. Capabilities of GPT-4 on medical challenge problems. arXiv:2303.13375. 2023. https://arxiv.org/abs/2303.13375
Singhal K, Azizi S, Tu T, Mahdavi S, Wei J, Chung HW, Scales N, Tanwani A, Cole-Lewis H, Pfohl S, et al. Large language models encode clinical knowledge. Nature. 2023;620:172-180. https://doi.org/10.1038/s41586-023-06291-2
Sun Y, Zhu C, Zheng S, Zhang K, Shui Z, Yu X, Zhao Y, Li H, Zhang Y, Zhao R, et al. PathAsst: redefining pathology through generative foundation AI assistant for pathology. arXiv:2305.15072. 2023. Accessed: September 28, 2023. https://arxiv.org/abs/2305.15072
Schukow C, Smith SC, Landgrebe E, Parasuraman S, Folaranmi OO, Paner GP, Amin MB. Application of ChatGPT in routine diagnostic pathology: promises, pitfalls, and potential future directions. Adv Anat Pathol. 2024;31(1):15-21. https://doi.org/10.1097/pap.0000000000000406
Qu L, Luo X, Fu K, Wang M, Song Z. The rise of AI language pathologists: exploring two-level prompt learning for few-shot weakly-supervised whole slide image classification. arXiv:2305.17891v1. 2023. https://arxiv.org/pdf/2305.17891v1.pdf
Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, Brogi E, Reuter VE, Klimstra DS, Fuchs TJ. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8): 1301-1309. https://doi.org/10.1038/s41591-019-0508-1
Berbís MA, McClintock DS, Bychkov A, Van der Laak J, Pantanowitz L, Lennerz JK, Cheng JY, Delahunt B, Egevad L, Eloy C, et al. Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade. eBioMedicine. 2023;88:104427. https://doi.org/10.1016/j.ebiom.2022.104427

Закрыть метаданные