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Early diagnosis of ALL is crucial for minimizing misdiagnosis, improving survival rates, and ensuring the implementation of precise treatment plans for patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>In this study, we propose a multi\u2010modal deep neural network\u2010based framework for early and efficient screening of ALL. Both white blood cell (WBC) scattergrams and complete blood count (CBC) are employed for ALL detection. The dataset comprises medical data from 233 patients with ALL, 283 patients with infectious mononucleosis (IM), and 183 healthy controls (HCs).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The combination of CBC data with WBC scattergrams achieved an accuracy of 98.43% in fivefold cross\u2010validation and a sensitivity of 96.67% in external validation, demonstrating the efficacy of our method. Additionally, the area under the curve (AUC) of this model surpasses 0.99, outperforming well\u2010trained medical technicians.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>To the best of our knowledge, this framework is the first to incorporate WBC scattergrams with CBC data for ALL screening, proving to be an efficient method with enhanced sensitivity and specificity. Integrating this framework into the screening procedure shows promise for improving the early diagnosis of ALL and reducing the burden on medical technicians. The code and dataset are available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/cvi-szu\/ALL-Screening\">https:\/\/github.com\/cvi\u2010szu\/ALL\u2010Screening<\/jats:ext-link>.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ijlh.14424","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T05:09:17Z","timestamp":1736917757000},"page":"454-462","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["An Efficient Acute Lymphoblastic Leukemia Screen Framework Based on Multi\u2010Modal Deep Neural Network"],"prefix":"10.1111","volume":"47","author":[{"given":"Qiuming","family":"Wang","sequence":"first","affiliation":[{"name":"Computer Vision Institute College of Computer Science and Software, Shenzhen University  China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Shenzhen Children's Hospital  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaojuan","family":"Luo","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Shenzhen Children's Hospital  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoling","family":"Luo","sequence":"additional","affiliation":[{"name":"Computer Vision Institute College of Computer Science and Software, Shenzhen University  China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuechen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Wuyi University  China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Shenzhen Children's Hospital  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Defa","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine Shenzhen Children's Hospital  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1420-0815","authenticated-orcid":false,"given":"Linlin","family":"Shen","sequence":"additional","affiliation":[{"name":"Computer Vision Institute College of Computer Science and Software, Shenzhen University  China"},{"name":"Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,1,14]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-016-2474-6"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMra1400972"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1111\/ped.13457"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.105999"},{"key":"e_1_2_11_6_1","first-page":"2750","volume-title":"The 5th Edition of the World Health Organization Classification of Haematolymphoid Tumours: Lymphoid Neoplasms","author":"Alaggio R.","year":"2022"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.4084\/mjhid.2014.073"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0889-8588(05)70183-0"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0145-2126(02)00034-6"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijlh.14305"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.blre.2023.101102"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00444-8"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.3233\/SHTI210863"},{"issue":"11","key":"e_1_2_11_14_1","first-page":"905","article-title":"Children N, s Epstein\u2010Barr Virus Infection Cooperative Group. 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