{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:43:39Z","timestamp":1753875819788,"version":"3.41.2"},"reference-count":22,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T00:00:00Z","timestamp":1749427200000},"content-version":"vor","delay-in-days":159,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Leptospirosis poses substantial challenges to global public health. In Malaysia, leptospirosis is endemic, with annual cases peaking during the monsoon season. The microscopic agglutination test (MAT) is the gold-standard serological method for confirmation of leptospirosis. However, it is labor-intensive and time-consuming, as it relies on the subjective interpretation of medical lab technicians. This study describes the development of a semiautomated workflow for Leptospira screening by integrating a TensorFlow and custom-designed Keras-based Deep Convolutional Neural Network (DCNN) with conventional MAT. We used a dataset of 442 positive and 442 negative MAT images, which consisted of a mixture of Leptospira serovars from Malaysia to train the model. The model was subjected to hyperparameter tuning, which modulated the number of convolutional layers, filters, kernel sizes, units in dense layers, activation functions, and learning rate. Verification of our tested model compared to the verified patient MAT results achieved the following metrics: a Precision score of 0.8125, a Recall of 0.9286, and an F1-Score of 0.8667. Combining our model with the current Malaysia Leptospira workflow can significantly speed up, reduce inaccuracies, and improve the management of leptospirosis. Furthermore, the application of this model is practical and adaptable, making it suitable for other labs that observe MAT as their Leptospira diagnosis. To our knowledge, this approach is Malaysia\u2019s first hybrid diagnostic approach for Leptospira diagnosis. Scaling up the dataset would enhance the model\u2019s accuracy, making it adaptable in other regions where leptospirosis is endemic.<\/jats:p>","DOI":"10.1093\/biomethods\/bpaf047","type":"journal-article","created":{"date-parts":[[2025,6,9]],"date-time":"2025-06-09T19:09:50Z","timestamp":1749496190000},"source":"Crossref","is-referenced-by-count":0,"title":["Enhancing leptospirosis screening using a deep convolutional neural network with microscopic agglutination test images"],"prefix":"10.1093","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2415-961X","authenticated-orcid":false,"given":"Murnihayati","family":"Hassan","sequence":"first","affiliation":[{"name":"Bacteriology Unit, Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health (MOH) , 40170 Setia Alam , Shah Alam, Selangor,","place":["Malaysia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siti Nur Zawani","family":"Rosli","sequence":"additional","affiliation":[{"name":"Bacteriology Unit, Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health (MOH) , 40170 Setia Alam , Shah Alam, Selangor,","place":["Malaysia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Natasya Amirah","family":"Mohamed Tahir","sequence":"additional","affiliation":[{"name":"Bacteriology Unit, Institute for Medical Research (IMR), National Institutes of Health (NIH), Ministry of Health (MOH) , 40170 Setia Alam , Shah Alam, Selangor,","place":["Malaysia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nurul Azmawati","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health Sciences, Universiti Sains Islam Malaysia , Bandar Baru Nilai , 71800, Nilai, Negeri Sembilan,","place":["Malaysia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khairunnisa","family":"Mohd Sukri","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health Sciences, Universiti Sains Islam Malaysia , Bandar Baru Nilai , 71800, Nilai, Negeri Sembilan,","place":["Malaysia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7149-8016","authenticated-orcid":false,"given":"Liyana","family":"Azmi","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health Sciences, Universiti Sains Islam Malaysia , Bandar Baru Nilai , 71800, Nilai, Negeri Sembilan,","place":["Malaysia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Norhasmira","family":"Mohammad","sequence":"additional","affiliation":[{"name":"Faculty of Cognitive Sciences & Human Development, Universiti Malaysia Sarawak, Jalan Datuk Mohammad Musa , 94300, Kota Samarahan, 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