Applied machine learning in hematopathology.
artificial intelligence
digital pathology
hematopathology
machine learning
whole slide imaging
Journal
International journal of laboratory hematology
ISSN: 1751-553X
Titre abrégé: Int J Lab Hematol
Pays: England
ID NLM: 101300213
Informations de publication
Date de publication:
Jun 2023
Jun 2023
Historique:
received:
09
03
2023
accepted:
12
05
2023
medline:
12
6
2023
pubmed:
1
6
2023
entrez:
31
5
2023
Statut:
ppublish
Résumé
An increasing number of machine learning applications are being developed and applied to digital pathology, including hematopathology. The goal of these modern computerized tools is often to support diagnostic workflows by extracting and summarizing information from multiple data sources, including digital images of human tissue. Hematopathology is inherently multimodal and can serve as an ideal case study for machine learning applications. However, hematopathology also poses unique challenges compared to other pathology subspecialities when applying machine learning approaches. By modeling the pathologist workflow and thinking process, machine learning algorithms may be designed to address practical and tangible problems in hematopathology. In this article, we discuss the current trends in machine learning in hematopathology. We review currently available machine learning enabled medical devices supporting hematopathology workflows. We then explore current machine learning research trends of the field with a focus on bone marrow cytology and histopathology, and how adoption of new machine learning tools may be enabled through the transition to digital pathology.
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
87-94Informations de copyright
© 2023 The Authors. International Journal of Laboratory Hematology published by John Wiley & Sons Ltd.
Références
Pena GP, Andrade-Filho JS. How does a pathologist make a diagnosis? Arch Pathol Lab Med. 2009;133(1):124-132. doi:10.5858/133.1.124
Salto-Tellez M, Maxwell P, Hamilton P. Artificial intelligence-the third revolution in pathology. Histopathology. 2019;74(3):372-376. doi:10.1111/his.13760
Chollet F. On the measure of intelligence. November 4, 2019. doi:10.48550/arxiv.1911.01547
Bell J. What is machine learning? Machine Learning and the City. Wiley; 2022:207-216. doi:10.1002/9781119815075.ch18
Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019;20(5):e253-e261. doi:10.1016/S1470-2045(19)30154-8
Baxi V, Edwards R, Montalto M, Saha S. Digital pathology and artificial intelligence in translational medicine and clinical practice. Mod Pathol. 2022;35(1):23-32. doi:10.1038/s41379-021-00919-2
Rodriguez JPM, Rodriguez R, Silva VWK, et al. Artificial intelligence as a tool for diagnosis in digital pathology whole slide images: a systematic review. J Pathol Inform. 2022;13:100138. doi:10.1016/j.jpi.2022.100138
Jahn SW, Plass M, Moinfar F. Digital pathology: advantages, limitations and emerging perspectives. J Clin Med. 2020;9(11):3697. doi:10.3390/jcm9113697
Madabhushi A. Digital pathology image analysis: opportunities and challenges. Imag Med. 2009;1(1):7-10. doi:10.2217/iim.09.9
Lin E, Fuda F, Luu HS, et al. Digital pathology and artificial intelligence as the next chapter in diagnostic hematopathology. Semin Diagn Pathol. 2023;40:88-94. doi:10.1053/j.semdp.2023.02.001
Lee SH, Erber WN, Porwit A, Tomonaga M, Peterson LC. ICSH guidelines for the standardization of bone marrow specimens and reports. Int J Lab Hematol. 2008;30(5):349-364. doi:10.1111/j.1751-553X.2008.01100.x
Bain BJ. Bone marrow aspiration. J Clin Pathol. 2001;54(9):657-663. doi:10.1136/jcp.54.9.657
Abdulrahman AA, Patel KH, Yang T, et al. Is a 500-cell count necessary for bone marrow differentials? Am J Clin Pathol. 2018;150(1):84-91. doi:10.1093/ajcp/aqy034
Bain BJ. The bone marrow aspirate of healthy subjects. Br J Haematol. 1996;94(1):206-209. doi:10.1046/j.1365-2141.1996.d01-1786.x
Nam S, Chong Y, Jung CK, et al. Introduction to digital pathology and computer-aided pathology. J Pathol Transl Med. 2020;54(2):125-134. doi:10.4132/jptm.2019.12.31
Kalra S, Tizhoosh HR, Choi C, et al. Yottixel - an image search engine for large archives of histopathology whole slide images. Med Image Anal. 2020;65:101757. doi:10.1016/j.media.2020.101757
Katz B, Feldman MD, Tessema M, et al. Evaluation of Scopio labs X100 full field PBS: the first high-resolution full field viewing of peripheral blood specimens combined with artificial intelligence-based morphological analysis. Int J Lab Hematol. 2021;43(6):1408-1416. doi:10.1111/ijlh.13681
Cornet E, Perol J-P, Troussard X. Performance evaluation and relevance of the CellaVisionTM DM96 system in routine analysis and in patients with malignant hematological diseases. Int J Lab Hematol. 2007;30(6):536-542. doi:10.1111/j.1751-553X.2007.00996.x
Lee LH, Mansoor A, Wood B, Nelson H, Higa D, Naugler C. Performance of CellaVision DM96 in leukocyte classification. J Pathol Inform. 2013;4(1):14. doi:10.4103/2153-3539.114205
Criel M, Godefroid M, Deckers B, Devos H, Cauwelier B, Emmerechts J. Evaluation of the red blood cell advanced software application on the CellaVision DM96. Int J Lab Hematol. 2016;38(4):366-374. doi:10.1111/ijlh.12497
Bachar N, Benbassat D, Brailovsky D, et al. An artificial intelligence-assisted diagnostic platform for rapid near-patient hematology. Am J Hematol. 2021;96(10):1264-1274. doi:10.1002/ajh.26295
Dale DC, Kelley ML, Navarro-de la Vega M, et al. A novel device suitable for home monitoring of white blood cell and neutrophil counts. Blood. 2018;132(Supplement 1):1103. doi:10.1182/blood-2018-99-112647
Fu X, Fu M, Li Q, et al. Morphogo: an automatic bone marrow cell classification system on digital images analyzed by artificial intelligence. Acta Cytol. 2020;64(6):588-596. doi:10.1159/000509524
Singh A, Ohgami RS. Super-resolution digital pathology image processing of bone marrow aspirate and cytology smears and tissue sections. J Pathol Inform. 2018;9:48. doi:10.4103/jpi.jpi_56_18
Chandradevan R, Aljudi AA, Drumheller BR, et al. Machine-based detection and classification for bone marrow aspirate differential counts: initial development focusing on nonneoplastic cells. Lab Investig. 2020;100(1):98-109. doi:10.1038/s41374-019-0325-7
Liu J, Yuan R, Li Y, et al. A deep learning method and device for bone marrow imaging cell detection. Ann Transl Med. 2022;10(4):208. doi:10.21037/atm-22-486
Tayebi RM, Mu Y, Dehkharghanian T, et al. Automated bone marrow cytology using deep learning to generate a histogram of cell types. Commun Med. 2022;2(1):45. doi:10.1038/s43856-022-00107-6
Lewis JE, Shebelut CW, Drumheller BR, et al. An automated pipeline for differential cell counts on whole-slide bone marrow aspirate smears. Mod Pathol. 2023;36(2):100003. doi:10.1016/j.modpat.2022.100003
Sirinukunwattana K, Aberdeen A, Theissen H, et al. Artificial intelligence-based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients. Blood Adv. 2020;4(14):3284-3294. doi:10.1182/BLOODADVANCES.2020002230
Lee N, Jeong S, Park MJ, Song W. Deep learning application of the discrimination of bone marrow aspiration cells in patients with myelodysplastic syndromes. Sci Rep. 2022;12(1):18677. doi:10.1038/s41598-022-21887-w
Tang G, Fu X, Wang Z, Chen M. A machine learning tool using digital microscopy (Morphogo) for the identification of abnormal lymphocytes in the bone marrow. Acta Cytol. 2021;65(4):354-357. doi:10.1159/000518382
van Eekelen L, Pinckaers H, van den Brand M, Hebeda KM, Litjens G. Using deep learning for quantification of cellularity and cell lineages in bone marrow biopsies and comparison to normal age-related variation. Pathology. 2022;54(3):318-327. doi:10.1016/j.pathol.2021.07.011
Eckardt J-N, Schmittmann T, Riechert S, et al. Deep learning identifies acute promyelocytic leukemia in bone marrow smears. BMC Cancer. 2022;22(1):201. doi:10.1186/s12885-022-09307-8
Eckardt J-N, Middeke JM, Riechert S, et al. Deep learning detects acute myeloid leukemia and predicts NPM1 mutation status from bone marrow smears. Leukemia. 2022;36(1):111-118. doi:10.1038/s41375-021-01408-w
Tizhoosh HR, Pantanowitz L. Artificial intelligence and digital pathology: challenges and opportunities. J Pathol Inform. 2018;9(1):38. doi:10.4103/jpi.jpi_53_18
Manescu P, Narayanan P, Bendkowski C, et al. Detection of acute promyelocytic leukemia in peripheral blood and bone marrow with annotation-free deep learning. Sci Rep. 2023;13(1):2562. doi:10.1038/s41598-023-29160-4
Rehman A, Abbas N, Saba T, Rahman SIU, Mehmood Z, Kolivand H. Classification of acute lymphoblastic leukemia using deep learning. Microsc Res Tech. 2018;81(11):1310-1317. doi:10.1002/jemt.23139
Vyshnav MT, Sowmya V, Gopalakrishnan EA, Variyar VVS, Menon VK, Soman KP. Deep learning based approach for multiple myeloma detection. Paper presented at: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2020, pp. 1-7. doi:10.1109/ICCCNT49239.2020.9225651
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(3):238-249. doi:10.1158/2643-3230.BCD-20-0162
Nicora G, Bellazzi R. A reliable machine learning approach applied to single-cell classification in acute myeloid leukemia. Paper presented at: AMIA Annual Symposium Proceedings; 2020, vol. 2020, pp. 925-932.
Deeb SJ, Tyanova S, Hummel M, Schmidt-Supprian M, Cox J, Mann M. Machine learning-based classification of diffuse large B-cell lymphoma patients by their protein expression profiles. Mol Cell Proteomics. 2015;14(11):2947-2960. doi:10.1074/mcp.M115.050245
Mosquera Orgueira A, González Pérez MS, Díaz Arias JÁ, et al. Survival prediction and treatment optimization of multiple myeloma patients using machine-learning models based on clinical and gene expression data. Leukemia. 2021;35(10):2924-2935. doi:10.1038/s41375-021-01286-2
Riasatian A, Babaie M, Maleki D, et al. Fine-tuning and training of densenet for histopathology image representation using TCGA diagnostic slides. Med Image Anal. 2021;70:102032. doi:10.1016/j.media.2021.102032
Chen C, Lu MY, Williamson DFK, Chen TY, Schaumberg AJ, Mahmood F. Fast and scalable search of whole-slide images via self-supervised deep learning. Nat Biomed Eng. 2022;6(12):1420-1434. doi:10.1038/s41551-022-00929-8
Lipkova J, Chen RJ, Chen B, et al. Artificial intelligence for multimodal data integration in oncology. Cancer Cell. 2022;40(10):1095-1110. doi:10.1016/j.ccell.2022.09.012
Hewer E. The Oncologist's guide to synoptic reporting: a primer. Oncology. 2020;98(6):396-402. doi:10.1159/000500884
Sever C, Abbott CL, de Baca ME, et al. Bone marrow synoptic reporting for hematologic neoplasms: guideline from the College of American Pathologists Pathology and Laboratory Quality Center. Arch Pathol Lab Med. 2016;140(9):932-949. doi:10.5858/arpa.2015-0450-SA
Mu Y, Tizhoosh HR, Tayebi RM, et al. A BERT model generates diagnostically relevant semantic embeddings from pathology synopses with active learning. Commun Med. 2021;1(1):11. doi:10.1038/s43856-021-00008-0
Mu Y, Tizhoosh HR, Dehkharghanian T, Campbell CJV. Whole slide image representation in bone marrow cytology. bioRxiv. 2022;2022.12.06.519318. doi:10.1101/2022.12.06.519318
Dehkharghanian T, Mu Y, Ross C, Sur M, Tizhoosh HR, Campbell CJV. Cell projection plots: a novel visualization of bone marrow aspirate cytology. bioRxiv. 2022;2022.12.06.519348. doi:10.1101/2022.12.06.519348
The DECIDE-AI Steering Group. DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence. Nat Med. 2021;27(2):186-187. doi:10.1038/s41591-021-01229-5
Sounderajah V, Ashrafian H, Golub RM, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open. 2021;11(6):e047709. doi:10.1136/bmjopen-2020-047709
Collins GS, Dhiman P, Andaur Navarro CL, et al. Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence. BMJ Open. 2021;11(7):e048008. doi:10.1136/bmjopen-2020-048008
Cheng JY, Abel JT, Balis UGJ, McClintock DS, Pantanowitz L. Challenges in the development, deployment, and regulation of artificial intelligence in anatomic pathology. Am J Pathol. 2021;191(10):1684-1692. doi:10.1016/j.ajpath.2020.10.018
Vellido A. The importance of interpretability and visualization in machine learning for applications in medicine and health care. Neural Comput Applic. 2020;32(24):18069-18083. doi:10.1007/s00521-019-04051-w
Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN. Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. Paper presented at: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Vol abs/1710.1, IEEE, pp. 839-847. doi:10.1109/WACV.2018.00097
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. Paper presented at: 2017 IEEE International Conference on Computer Vision (ICCV), Vol abs/1610.0, IEEE pp. 618-626. doi:10.1109/ICCV.2017.74
Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Heal. 2021;3(11):e745-e750. doi:10.1016/S2589-7500(21)00208-9
Dehkharghanian T, Bidgoli AA, Riasatian A, et al. Biased data, Biased AI: Deep networks predict the acquisition site of TCGA images. Diagn Pathol. 2023;18(1):6. doi:10.21203/RS.3.RS-943804/V1