{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T22:07:55Z","timestamp":1793484475434,"version":"4.5.0"},"reference-count":34,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["DIGITAL HEALTH"],"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Acute lymphoblastic leukemia (ALL), certain types of which are present in children, is a common childhood cancer and must be diagnosed correctly. The impact of early diagnosis on patient survival is high due to timely and appropriate treatment. However, current ALL diagnostic procedures remain laborious and prone to inaccuracy. This study aims to enhance automated ALL diagnosis by introducing a Hybrid Multi-Scale Contextual Attention Module (HMSCAM), for improved detection and subtype classification from peripheral blood smear (PBS) images. The objectives of thisstudy can be summed up in terms of capturing varying morphological features, modelling long term dependencies and dynamic recalibration of input-specific adaptation for effective classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A pretrained ResNet50 backbone augmented with HMSCAM module is used to classify ALL into benign, Early Pre-B, Pre-B, and Pro-B categories. This enables real-time automated diagnostic feedback, with experiments conducted on 3256 PBS images from 89 patients to validate the methodology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The evaluation of the model on the 3256 PBS images against binary classification task yielded an accuracy of 96.5%. Whereas multiclassification task accuracy is 93.5%, with AUC values for the binary and multiclassification tasks ranging from 0.96 to 0.97. The HMSCAM framework from this study outperformed the benchmark models with average inference time being 0.040\u2005s per image which supports real-time clinical applicability.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>HMSCAM serves as advanced, adaptable and effective computer-aided diagnosis system. This system reduces baseline diagnostic errors while supporting personalized treatment planning for ALL. The integration of interpretable mechanisms further facilitates clinical adoption of artificial intelligence-enabled diagnostic technologies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/20552076261428734","type":"journal-article","created":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T21:23:50Z","timestamp":1772141030000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Multiscale contextual attention network for robust diagnosis of acute lymphoblastic leukemia in blood smears: Implications for clinical adoption and the role of medical science liaisons"],"prefix":"10.1177","volume":"12","author":[{"given":"Ali","family":"Waqar","sequence":"first","affiliation":[{"name":"Quanzhou University of Information Engineering"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Samuel","family":"Dyer","sequence":"additional","affiliation":[{"name":"Medical Science Liaison Society, Miami, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Abubakar","sequence":"additional","affiliation":[{"name":"Lahore Garrison University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nabeel Ahmed","family":"Khan","sequence":"additional","affiliation":[{"name":"Riphah International University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akbare","family":"Yaqub","sequence":"additional","affiliation":[{"name":"FAST National University of Computer and Emerging Sciences Lahore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5587-7614","authenticated-orcid":false,"given":"Zohaib","family":"Mushtaq","sequence":"additional","affiliation":[{"name":"University of Sargodha"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1400972"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.24862"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2014.59.1636"},{"key":"e_1_3_2_5_2","volume-title":"WHO classification of tumours of haematopoietic and lymphoid tissues","author":"Swerdlow SH","year":"2017","unstructured":"Swerdlow SH, Campo E, Harris NL, et al. WHO classification of tumours of haematopoietic and lymphoid tissues. Rev 4th ed. 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A deep learning-based approach for the diagnosis of acute lymphoblastic leukemia. J Med Syst 2022; 11: 1\u201317.","journal-title":"J Med Syst"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.array.2025.100397"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-14212-0"},{"key":"e_1_3_2_16_2","first-page":"5998","volume-title":"Advances in neural information processing systems","author":"Vaswani A","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. In: Advances in neural information processing systems. Red Hook, NY, USA: Curran Associates Inc., 2017, pp.5998\u20136008."},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-12792-5"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00813"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1140\/epjp\/s13360-023-04786-5"},{"key":"e_1_3_2_22_2","volume-title":"Advances in neural information processing systems","author":"Wu H","year":"2021","unstructured":"Wu H, Xu J, Wang J, et al. Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. In: Advances in neural information processing systems. 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Medical Science Liaison Corporation 2023: 1\u2013236.","journal-title":"Medical Science Liaison Corporation"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.3390\/pharmacy13020051"},{"key":"e_1_3_2_30_2","unstructured":"Medical Science Liaison Society. Medical science liaison activity guidelines. Version 2.0. Miami FL: MSL Society 2024. Available at: https:\/\/www.themsls.org\/msl-guidelines\/ (accessed 18 January 2025)."},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2022.103793"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1002\/jemt.23139"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11193168"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.3389\/frai.2025.1620252"},{"key":"e_1_3_2_35_2","first-page":"1","article-title":"Deep learning with self-attention and enhanced preprocessing for precise diagnosis of acute lymphoblastic leukemia from bone marrow smears in hemato-oncology","volume":"2508","author":"Maruf M","year":"2025","unstructured":"Maruf M, Haque MM, Paul B. Deep learning with self-attention and enhanced preprocessing for precise diagnosis of acute lymphoblastic leukemia from bone marrow smears in hemato-oncology. arXiv 2025; abs\/2508.17216: 1\u201326. Available at: https:\/\/arxiv.org\/abs\/2508.17216.","journal-title":"arXiv"}],"container-title":["DIGITAL HEALTH"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076261428734","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/20552076261428734","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/20552076261428734","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T17:56:15Z","timestamp":1777398975000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/20552076261428734"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":34,"alternative-id":["10.1177\/20552076261428734"],"URL":"https:\/\/doi.org\/10.1177\/20552076261428734","relation":{},"ISSN":["2055-2076","2055-2076"],"issn-type":[{"value":"2055-2076","type":"print"},{"value":"2055-2076","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2]]},"article-number":"20552076261428734"}}