{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,31]],"date-time":"2026-10-31T22:28:21Z","timestamp":1793485701978,"version":"4.5.0"},"reference-count":14,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:00:00Z","timestamp":1774051200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":40,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"University of Colombo School of Computing"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Thalassemia is the most prevalent single-gene disorder in Sri Lanka, imposing a significant socioeconomic and healthcare burden. Early carrier detection is essential for genetic counselling and the prevention of thalassemia major births. Current screening programs rely on red cell indices, manual examination of blood smears by expert haematologists, and expensive confirmatory tests such as High-Performance Liquid Chromatography (HPLC) or genetic analysis, which limit large-scale applicability in low-resource settings. This study aims to develop a machine learning\u2013based automated screening tool that integrates Red Blood Cell (RBC) indices and blood smear image analysis to support cost-effective and scalable \u03b2-thalassemia trait detection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In a cross-sectional study of 152 individuals (54 confirmed \u03b2-thalassemia trait, 98 negative), 30% of the dataset was allocated for independent testing. The remaining 70% was used for model training, with 5-fold cross-validation for the RBC analysis model and 10-fold cross-validation for the image analysis model. RBC features were classified using a Multi-Layer Perceptron (MLP; scikit-learn), while blood smear images, captured through a conventional microscope using a smartphone camera, were analysed using a transfer learning\u2013based VGG-16 CNN (TensorFlow\/Keras). Data balancing and image augmentation (rotation, flipping, brightness variation) were applied to address class imbalance and overfitting. A two-step screening pipeline was proposed, applying RBC analysis first, followed by smear image analysis for RBC-negative cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The RBC indices analysis model achieved 88.2% sensitivity (95% CI: 63.6\u201398.5%) and 92.9% specificity (95% CI: 76.5\u201399.1%), while the image analysis model reached 88.2% sensitivity (95% CI: 63.6\u201398.5%) and 64.3% specificity (95% CI: 44.1\u201381.4%). When integrated sequentially, the combined pipeline achieved 100% sensitivity (95% CI: 80.5\u2013100%) and 60.7% specificity (95% CI: 40.6\u201378.5%), reducing the overall need for smear preparation by 37.7%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This enhanced dual-modal screening system provides a highly sensitive, potential to be cost-effective, and practical solution for \u03b2-thalassemia carrier detection, enabling mass screening and supporting sustainable prevention strategies in resource-limited settings. With larger, independent, multi-centre validation, it could be integrated into laboratory workflows to expand screening coverage, while ongoing evaluation improves generalisability and inform policy adoption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial registration<\/jats:title>\n                    <jats:p>Not applicable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-026-03451-y","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T10:03:53Z","timestamp":1774087433000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A dual-modal machine learning framework integrating red blood indices and smartphone-captured microscopic images for \u03b2-thalassemia screening"],"prefix":"10.1186","volume":"26","author":[{"given":"C. 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Trends Pediatr. 2023;2(1):1\u20137. https:\/\/doi.org\/10.5222\/TP.2021.10820.","journal-title":"Trends Pediatr"},{"issue":"1","key":"3451_CR2","first-page":"69","volume":"9","author":"O Perera","year":"2020","unstructured":"Perera O, Ranathunga T. Socio-economic impact of thalassemia: a case study of Kurunegala district, Sri Lanka. Fac J Humanit Social Sci (Univ Ruhuna). 2020;9(1):69\u201381. (hss.ruh.ac.lk).","journal-title":"Fac J Humanit Social Sci (Univ Ruhuna)"},{"issue":"1","key":"3451_CR3","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1080\/08998280.2007.11928230","volume":"20","author":"AJ Marengo-Rowe","year":"2007","unstructured":"Marengo-Rowe AJ. The thalassemias and related disorders. Proc (Bayl Univ Med Cent). 2007;20(1):27\u201330. https:\/\/doi.org\/10.1080\/08998280.2007.11928230.","journal-title":"Proc (Bayl Univ Med Cent)"},{"issue":"4","key":"3451_CR4","doi-asserted-by":"publisher","first-page":"135","DOI":"10.3390\/thalassrep12040018","volume":"12","author":"N Amarasinghe","year":"2022","unstructured":"Amarasinghe N, et al. Redesigning new policy options for thalassemia prevention in Sri Lanka. Thalassemia Rep. 2022;12(4):135\u201342. https:\/\/doi.org\/10.3390\/thalassrep12040018.","journal-title":"Thalassemia Rep"},{"key":"3451_CR5","doi-asserted-by":"publisher","unstructured":"de Silva S, Fisher CA, Premawardhena A, Lamabadusuriya SP, Peto TE, Perera G, Old JM, Clegg JB, Olivieri NF, Weatherall DJ, Sri Lanka Thalassaemia Study Group. Thalassaemia in Sri Lanka: implications for the future health burden of Asian populations. Lancet. 2000;355(9206):786\u2009\u2013\u200991. https:\/\/doi.org\/10.1016\/S0140-67369908246-X. 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IEEE Access. 2021;9:45528\u201338. https:\/\/doi.org\/10.1109\/ACCESS.2021.3066782.","journal-title":"IEEE Access"},{"key":"3451_CR14","doi-asserted-by":"crossref","first-page":"10418","DOI":"10.1016\/j.matpr.2020.12.959","volume":"46","author":"S Purwar","year":"2021","unstructured":"Purwar S, Singh P, Singh A. Classification of thalassemia patients using a fusion of deep image and clinical features. 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All procedures performed in this study involving human participants were conducted in accordance with the ethical standards of the Institutional Ethical Review Committee and with the 1964 Declaration of Helsinki and its later amendments. Written informed consent was obtained from all participants prior to enrolment.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"148"}}