{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T17:18:45Z","timestamp":1788369525098,"version":"build-2803163510"},"reference-count":114,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005743","name":"Universit\u00e0 Cattolica del Sacro Cuore","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005743","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009880","name":"Regione Lazio","doi-asserted-by":"publisher","award":["QUaD2: Una piattaforma e-Health potenziata da algoritmi di machine learning QUantistico per la prevenzione di complicazioni macrovascolari e microvascolari nel Diabete di tipo 2"],"award-info":[{"award-number":["QUaD2: Una piattaforma e-Health potenziata da algoritmi di machine learning QUantistico per la prevenzione di complicazioni macrovascolari e microvascolari nel Diabete di tipo 2"]}],"id":[{"id":"10.13039\/501100009880","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.cmpb.2026.109609","type":"journal-article","created":{"date-parts":[[2026,8,16]],"date-time":"2026-08-16T14:10:39Z","timestamp":1786889439000},"page":"109609","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Quantum machine learning in diabetes and metabolism: Applications and translational challenges"],"prefix":"10.1016","volume":"286","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5299-7660","authenticated-orcid":false,"given":"Alessia","family":"Riente","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7306-8004","authenticated-orcid":false,"given":"Cassandra","family":"Serantoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7712-7576","authenticated-orcid":false,"given":"Michele Maria","family":"De Giulio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1857-5043","authenticated-orcid":false,"given":"Stefano","family":"Capezzone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosita","family":"Esposito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4260-5107","authenticated-orcid":false,"given":"Marco","family":"De Spirito","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2154-319X","authenticated-orcid":false,"given":"Giuseppe","family":"Maulucci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"7671","key":"10.1016\/j.cmpb.2026.109609_bib0001","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/nature23474","article-title":"Quantum machine learning","volume":"549","author":"Biamonte","year":"2017","journal-title":"Nature"},{"key":"10.1016\/j.cmpb.2026.109609_bib0002","doi-asserted-by":"crossref","unstructured":"Schuld M., Petruccione F. Quantum science and technology supervised learning with Quantum computers [Internet]. 2018. Available from: http:\/\/www.springer.com\/series\/10039.","DOI":"10.1007\/978-3-319-96424-9"},{"key":"10.1016\/j.cmpb.2026.109609_bib0003","series-title":"Big Data Analytics in Healthcare: Promise and Potential. Health Information Science and Systems","author":"Raghupathi","year":"2014"},{"key":"10.1016\/j.cmpb.2026.109609_bib0004","series-title":"Deep Learning. Nature","author":"Lecun","year":"2015"},{"key":"10.1016\/j.cmpb.2026.109609_bib0005","series-title":"Quantum Computation and Quantum Information","author":"Nielsen Michael","year":"2010"},{"issue":"3","key":"10.1016\/j.cmpb.2026.109609_bib0006","article-title":"Quantum support vector machine for big data classification","volume":"113","author":"Rebentrost","year":"2014","journal-title":"Phys. Rev. Lett."},{"issue":"7747","key":"10.1016\/j.cmpb.2026.109609_bib0007","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1038\/s41586-019-0980-2","article-title":"Supervised learning with quantum-enhanced feature spaces","volume":"567","author":"Havl\u00ed\u010dek","year":"2019","journal-title":"Nature"},{"issue":"4","key":"10.1016\/j.cmpb.2026.109609_bib0008","article-title":"Parameterized quantum circuits as machine learning models","volume":"4","author":"Benedetti","year":"2019","journal-title":"Quant. Sci. Technol."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109609_bib0009","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1093\/ndt\/gfaf177","article-title":"IDF diabetes atlas 11th edition 2025: global prevalence and projections for 2050","volume":"41","author":"Duncan","year":"2026","journal-title":"Nephrol. Dial. Transplant."},{"key":"10.1016\/j.cmpb.2026.109609_bib0010","doi-asserted-by":"crossref","first-page":"1640","DOI":"10.1161\/CIRCULATIONAHA.109.192644","author":"Alberti","year":"2009","journal-title":"Circulation"},{"key":"10.1016\/j.cmpb.2026.109609_bib0011","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1152\/physrev.00045.2011","article-title":"Mechanisms of diabetic complications","volume":"93","author":"Forbes","year":"2013","journal-title":"Physiol. Rev."},{"key":"10.1016\/j.cmpb.2026.109609_bib0012","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1056\/NEJMra0906948","article-title":"Genomic medicine genomics, type 2 diabetes, and obesity","volume":"363","author":"Mccarthy","year":"2010","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.cmpb.2026.109609_bib0013","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.csbj.2016.12.005","article-title":"Machine learning and data mining methods in diabetes research","volume":"15","author":"Kavakiotis","year":"2017","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.cmpb.2026.109609_bib0014","unstructured":"Hancco-Quispe J.K., Borda-Colque J.P., Torres-Cruz F. Quantum machine learning applied to the classification of diabetes [Internet]. 2022 Dec 31. Available from: http:\/\/arxiv.org\/abs\/2301.00109."},{"key":"10.1016\/j.cmpb.2026.109609_bib0015","series-title":"Proceedings of the 12th International Conference on Ubiquitous and Future Networks (ICUFN)","first-page":"413","article-title":"Quantum neural networks: concepts, applications, and challenges","author":"Kwak","year":"2021"},{"issue":"10","key":"10.1016\/j.cmpb.2026.109609_bib0016","doi-asserted-by":"crossref","first-page":"2705","DOI":"10.3390\/cancers15102705","article-title":"Hybrid quantum neural network for drug response prediction","volume":"15","author":"Sagingalieva","year":"2023","journal-title":"Cancers"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109609_bib0017","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1177\/19322968221103561","article-title":"Explainable machine learning for real-time hypoglycemia and hyperglycemia prediction and personalized control recommendations","volume":"18","author":"Duckworth","year":"2024","journal-title":"J. Diabetes Sci. Technol."},{"issue":"3\u20134","key":"10.1016\/j.cmpb.2026.109609_bib0018","first-page":"316","article-title":"Quantum algorithms for nearest-neighbor methods for supervised and unsupervised learning","volume":"15","author":"Wiebe","year":"2015","journal-title":"Quant. Inf. Comput."},{"key":"10.1016\/j.cmpb.2026.109609_bib0019","doi-asserted-by":"crossref","first-page":"11423","DOI":"10.1109\/ACCESS.2024.3353461","article-title":"Quantum machine learning revolution in healthcare: a systematic review of emerging perspectives and applications","volume":"12","author":"Ullah","year":"2024","journal-title":"IEEE Access"},{"key":"10.1016\/j.cmpb.2026.109609_bib0020","series-title":"Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review. IEEE Access","first-page":"80463","author":"Maheshwari","year":"2022"},{"key":"10.1016\/j.cmpb.2026.109609_bib0021","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.infsof.2018.09.006","article-title":"Guidelines for including grey literature and conducting multivocal literature reviews in software engineering","volume":"106","author":"Garousi","year":"2019","journal-title":"Inf. Softw. Technol."},{"key":"10.1016\/j.cmpb.2026.109609_bib0022","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.jss.2018.09.082","article-title":"The pains and gains of microservices: a systematic grey literature review","volume":"146","author":"Soldani","year":"2018","journal-title":"J. Syst. Softw."},{"key":"10.1016\/j.cmpb.2026.109609_bib0023","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/nmeth.4551","article-title":"Points of significance: machine learning: supervised methods","volume":"15","author":"Bzdok","year":"2018","journal-title":"Nat. Methods"},{"issue":"2","key":"10.1016\/j.cmpb.2026.109609_bib0024","doi-asserted-by":"crossref","first-page":"5","DOI":"10.30564\/ssid.v2i2.1931","article-title":"Machine learning: a review","volume":"2","author":"Udousoro","year":"2020","journal-title":"Semiconduct. Sci. Inf. Dev."},{"key":"10.1016\/j.cmpb.2026.109609_bib0025","unstructured":"Arora S., Barak B. Computational complexity: a modern approach [Internet]. 2006. Available from: http:\/\/www.cs.princeton.edu\/theory\/complexity\/."},{"key":"10.1016\/j.cmpb.2026.109609_bib0026","doi-asserted-by":"crossref","unstructured":"David Mermin N. Quantum computer science: an introduction. 2007.","DOI":"10.1017\/CBO9780511813870"},{"key":"10.1016\/j.cmpb.2026.109609_bib0027","doi-asserted-by":"crossref","first-page":"219275","DOI":"10.1109\/ACCESS.2020.3041719","article-title":"Machine learning: quantum vs classical","volume":"8","author":"Khan","year":"2020","journal-title":"IEEE Access."},{"key":"10.1016\/j.cmpb.2026.109609_bib0028","series-title":"Quantum Processes, Systems, and Information","first-page":"469","author":"Benjamin","year":"2010"},{"key":"10.1016\/j.cmpb.2026.109609_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116512","article-title":"Machine learning in the quantum realm: the state-of-the-art, challenges, and future vision","volume":"194","author":"Houssein","year":"2022","journal-title":"Expert. Syst. Appl."},{"issue":"4","key":"10.1016\/j.cmpb.2026.109609_bib0030","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevLett.122.040504","article-title":"Quantum machine learning in feature hilbert spaces","volume":"122","author":"Schuld","year":"2019","journal-title":"Phys. Rev. Lett."},{"key":"10.1016\/j.cmpb.2026.109609_bib0031","doi-asserted-by":"crossref","unstructured":"Schuld M., Sweke R., Meyer J.J. The effect of data encoding on the expressive power of variational quantum machine learning models [Internet]. 2021 Mar 9. doi:10.1103\/PhysRevA.103.032430.","DOI":"10.1103\/PhysRevA.103.032430"},{"key":"10.1016\/j.cmpb.2026.109609_bib0032","doi-asserted-by":"crossref","first-page":"226","DOI":"10.22331\/q-2020-02-06-226","article-title":"Data re-uploading for a universal quantum classifier","volume":"4","author":"P\u00e9rez-Salinas","year":"2020","journal-title":"Quantum"},{"issue":"3","key":"10.1016\/j.cmpb.2026.109609_bib0033","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevA.98.032309","article-title":"Quantum circuit learning","volume":"98","author":"Mitarai","year":"2018","journal-title":"Phys. Rev. A"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109609_bib0034","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-021-27045-6","article-title":"Noise-induced barren plateaus in variational quantum algorithms","volume":"12","author":"Wang","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.cmpb.2026.109609_bib0035","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1038\/s42254-021-00348-9","article-title":"Variational quantum algorithms","volume":"3","author":"Cerezo","year":"2021","journal-title":"Nat. Rev. Phys."},{"issue":"6245","key":"10.1016\/j.cmpb.2026.109609_bib0036","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"2015","journal-title":"Science"},{"key":"10.1016\/j.cmpb.2026.109609_bib0037","series-title":"The Elements of Statistical Learning Data Mining, Inference, and Prediction","author":"Hastie","year":"2008"},{"key":"10.1016\/j.cmpb.2026.109609_bib0038","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNN.2005.845141","article-title":"Survey of clustering algorithms","volume":"16","author":"Xu","year":"2005","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.cmpb.2026.109609_bib0039","unstructured":"Yu C., Liu J., Nemati S. Reinforcement learning in healthcare: a survey [Internet]. 2020 Apr 24. Available from: http:\/\/arxiv.org\/abs\/1908.08796."},{"key":"10.1016\/j.cmpb.2026.109609_bib0040","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1038\/s41591-018-0316-z","article-title":"A guide to deep learning in healthcare","volume":"25","author":"Esteva","year":"2019","journal-title":"Nat. Med."},{"key":"10.1016\/j.cmpb.2026.109609_bib0041","unstructured":"Shor P.W. Algorithms for quantum computation: discrete logarithms and factoring. 1994."},{"key":"10.1016\/j.cmpb.2026.109609_bib0042","doi-asserted-by":"crossref","unstructured":"Grover L.K. A fast quantum mechanical algorithm for database search. 1996.","DOI":"10.1145\/237814.237866"},{"issue":"15","key":"10.1016\/j.cmpb.2026.109609_bib0043","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevLett.103.150502","article-title":"Quantum algorithm for linear systems of equations","volume":"103","author":"Harrow","year":"2009","journal-title":"Phys. Rev. Lett."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109609_bib0044","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-018-07090-4","article-title":"Barren plateaus in quantum neural network training landscapes","volume":"9","author":"McClean","year":"2018","journal-title":"Nat. Commun."},{"key":"10.1016\/j.cmpb.2026.109609_bib0045","doi-asserted-by":"crossref","unstructured":"Schuld M., Sinayskiy I., Petruccione F. An introduction to quantum machine learning [Internet]. 2015. doi:10.1080\/00107514.2014.964942.","DOI":"10.1007\/978-1-4899-7502-7_913-1"},{"key":"10.1016\/j.cmpb.2026.109609_bib0046","unstructured":"Farhi E., Neven H. Classification with quantum neural networks on near term processors [Internet]. 2018 Feb 16. Available from: http:\/\/arxiv.org\/abs\/1802.06002."},{"issue":"9","key":"10.1016\/j.cmpb.2026.109609_bib0047","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1038\/nphys3029","article-title":"Quantum principal component analysis","volume":"10","author":"Lloyd","year":"2014","journal-title":"Nat. Phys."},{"key":"10.1016\/j.cmpb.2026.109609_bib0048","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1007\/s11128-020-02770-x","article-title":"Quantum algorithm for help-training semi-supervised support vector machine","volume":"19","author":"Hou","year":"2020","journal-title":"Quant. Inf. Process."},{"key":"10.1016\/j.cmpb.2026.109609_bib0049","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1161\/ATVBAHA.107.151092","article-title":"Metabolic syndrome pandemic","volume":"28","author":"Grundy","year":"2008","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"10.1016\/j.cmpb.2026.109609_bib0050","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1210\/er.2008-0024","article-title":"The metabolic syndrome","volume":"29","author":"Cornier","year":"2008","journal-title":"Endocr. Rev."},{"key":"10.1016\/j.cmpb.2026.109609_bib0051","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1038\/s41574-019-0176-8","article-title":"Obesity: global epidemiology and pathogenesis","volume":"15","author":"Bl\u00fcher","year":"2019","journal-title":"Nat. Rev. Endocrinol."},{"key":"10.1016\/j.cmpb.2026.109609_bib0052","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.129045","article-title":"Quantum metabolic Avatar: a digital replica of metabolism enhanced by quantum algorithms","volume":"296","author":"Abeltino","year":"2026","journal-title":"Expert. Syst. Appl."},{"issue":"3","key":"10.1016\/j.cmpb.2026.109609_bib0053","doi-asserted-by":"crossref","first-page":"206","DOI":"10.17694\/bajece.1475896","article-title":"The effect of linear discriminant analysis and quantum feature maps on QSVM performance for obesity diagnosis","volume":"12","author":"\u00d6zpolat","year":"2024","journal-title":"Balkan J. Electr. Comput. Eng."},{"key":"10.1016\/j.cmpb.2026.109609_bib0054","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1016\/S0140-6736(24)02317-1","article-title":"Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: a pooled analysis of 1108 population-representative studies with 141 million participants NCD risk factor collaboration (NCD-RisC)*","volume":"404","author":"Zhou","year":"2024","journal-title":"Lancet"},{"key":"10.1016\/j.cmpb.2026.109609_bib0055","series-title":"Proceedings of the IEEE Symposium Series on Computational Intelligence, SSCI 2021 - Proceedings","article-title":"Diabetes prediction using quantum neurons with preprocessing based on hypercomplex numbers","author":"Monteiro","year":"2021"},{"issue":"4","key":"10.1016\/j.cmpb.2026.109609_bib0056","doi-asserted-by":"crossref","first-page":"3073","DOI":"10.1007\/s40747-021-00398-7","article-title":"Comparative performance analysis of quantum machine learning with deep learning for diabetes prediction","volume":"8","author":"Gupta","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.cmpb.2026.109609_bib0057","series-title":"Proceedings of the ITU Kaleidoscope: Innovation and Digital Transformation For a Sustainable World (ITU K)","article-title":"PIMA diabetes prediction using machine learning and quantum machine learning techniques","author":"Dixit","year":"2024"},{"key":"10.1016\/j.cmpb.2026.109609_bib0058","series-title":"Performance Analysis of Quantum and Classical Machine Learning Models For Feature Selection and Classification of the Diabetes Health Indicators Dataset","first-page":"1","author":"Gen\u00c7","year":"2024"},{"key":"10.1016\/j.cmpb.2026.109609_bib0059","series-title":"Proceedings of the IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2020","article-title":"Machine learning applied to diabetes dataset using quantum versus classical computation","author":"Maheshwari","year":"2020"},{"issue":"2","key":"10.1016\/j.cmpb.2026.109609_bib0060","first-page":"1849","article-title":"Diabetes type 2: poincar\u00e9 data preprocessing for quantum machine learning","volume":"67","author":"Sierra-Sosa","year":"2021","journal-title":"Comput. Mater. Cont."},{"key":"10.1016\/j.cmpb.2026.109609_bib0061","series-title":"Proceedings of the International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023","first-page":"1178","article-title":"Comparative performance analysis of quantum algorithm with machine learning algorithms on diabetes mellitus","author":"Prakash","year":"2023"},{"key":"10.1016\/j.cmpb.2026.109609_bib0062","doi-asserted-by":"crossref","unstructured":"Jha S.K., Adhikari N.B. Analysis of ensembled learning using classical and quantum boosting approaches for diabetes mellitus prediction [Internet]. 2024. Available from: https:\/\/www.researchsquare.com\/article\/rs-4199185\/v1 10.21203\/rs.3.rs-4199185\/v1.","DOI":"10.21203\/rs.3.rs-4199185\/v1"},{"key":"10.1016\/j.cmpb.2026.109609_bib0063","article-title":"Noninvasive estimation of blood glucose and HbA1c using quantum machine learning technique","volume":"19","author":"Sridevi","year":"2025","journal-title":"Mach. Learn. Appl."},{"issue":"14","key":"10.1016\/j.cmpb.2026.109609_bib0064","doi-asserted-by":"crossref","first-page":"1113","DOI":"10.1016\/j.jacc.2010.05.034","article-title":"The metabolic syndrome and cardiovascular risk: a systematic review and meta-analysis","volume":"56","author":"Mottillo","year":"2010","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.cmpb.2026.109609_bib0065","series-title":"Quantum Machine Learning in Ophthalmology. Eye (Basingstoke)","first-page":"2857","author":"Masalkhi","year":"2024"},{"key":"10.1016\/j.cmpb.2026.109609_bib0066","unstructured":"Alavi A., Akhoundi H. Hybrid classical-quantum method for diabetic foot ulcer classification [Internet]. 2021 Oct 5. Available from: http:\/\/arxiv.org\/abs\/2110.02222."},{"key":"10.1016\/j.cmpb.2026.109609_bib0067","doi-asserted-by":"crossref","DOI":"10.1016\/j.mex.2025.103185","article-title":"QuantumNet: an enhanced diabetic retinopathy detection model using classical deep learning-quantum transfer learning","volume":"14","author":"Bali","year":"2025","journal-title":"MethodsX"},{"key":"10.1016\/j.cmpb.2026.109609_bib0068","unstructured":"Jain A., Gupta R., Singhal J. Diabetic retinopathy detection using quantum transfer learning [Internet]. 2024 May 2. Available from: http:\/\/arxiv.org\/abs\/2405.01734."},{"issue":"3","key":"10.1016\/j.cmpb.2026.109609_bib0069","doi-asserted-by":"crossref","first-page":"18","DOI":"10.53388\/MDM202508018","article-title":"Quantum-assisted early detection of diabetic retinopathy: a novel integration of quantum machine learning in biomedical imaging","volume":"8","author":"Acu\u00f1a","year":"2025","journal-title":"Med. Data Min."},{"issue":"9","key":"10.1016\/j.cmpb.2026.109609_bib0070","doi-asserted-by":"crossref","first-page":"2008","DOI":"10.3390\/math11092008","article-title":"Quantum computing meets deep learning: a promising approach for diabetic retinopathy classification","volume":"11","author":"Alsubai","year":"2023","journal-title":"Mathematics"},{"key":"10.1016\/j.cmpb.2026.109609_bib0071","first-page":"2625","article-title":"VF-QCNN: diabetic retinopathy detection using Volans Finch optimization-based quantum convolutional neural network model","volume":"4","author":"Kalkonde","year":"2026","journal-title":"Biomed. Mater. Dev."},{"key":"10.1016\/j.cmpb.2026.109609_bib0072","series-title":"Chronic Kidney Disease and Type 2 Diabetes","article-title":"The interplay between diabetes, cardiovascular disease, and kidney disease","author":"Usman","year":"2021"},{"key":"10.1016\/j.cmpb.2026.109609_bib0073","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.2789","article-title":"Variational quantum classifier-based early identification and classification of chronic kidney disease using sparse autoencoder and LASSO shrinkage","volume":"11","author":"Parthasarathi","year":"2025","journal-title":"PeerJ Comput. Sci."},{"key":"10.1016\/j.cmpb.2026.109609_bib0074","first-page":"41","article-title":"Quantum-enhanced machine learning algorithms for heart disease prediction","volume":"13","author":"Alotaibi","year":"2023","journal-title":"Hum.-Cent. Comput. Inf. Sci."},{"key":"10.1016\/j.cmpb.2026.109609_bib0075","article-title":"Quantum machine learning applied to electronic healthcare records for ischemic Heart disease classification","volume":"13","author":"Maheshwari","year":"2023","journal-title":"Hum.-Cent. Comput. Inf. Sci."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109609_bib0076","doi-asserted-by":"crossref","first-page":"761","DOI":"10.32604\/iasc.2023.032262","article-title":"Explainable heart disease prediction using ensemble-quantum machine learning approach","volume":"36","author":"Abdulsalam","year":"2023","journal-title":"Intell. Autom. Soft Comput."},{"key":"10.1016\/j.cmpb.2026.109609_bib0077","doi-asserted-by":"crossref","first-page":"136122","DOI":"10.1109\/ACCESS.2023.3338145","article-title":"QuCardio: application of quantum machine learning for detection of cardiovascular diseases","volume":"11","author":"Prabhu","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.cmpb.2026.109609_bib0078","series-title":"Proceedings of the IEEE International Conference on Quantum Computing and Engineering (QCE)","first-page":"258","article-title":"Early heart disease prediction using hybrid quantum classification","author":"Heidari","year":"2023"},{"issue":"5","key":"10.1016\/j.cmpb.2026.109609_bib0079","article-title":"Predictive analysis of heart disease using quantum-assisted machine learning","volume":"7","author":"Banday","year":"2025","journal-title":"Discover Appl. Sci."},{"key":"10.1016\/j.cmpb.2026.109609_bib0080","article-title":"Alzheimer\u2019s disease is type 3 diabetes-evidence reviewed","author":"Monte","year":"2008","journal-title":"J. Diabetes Sci. Technol."},{"key":"10.1016\/j.cmpb.2026.109609_bib0081","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.neubiorev.2016.03.005","article-title":"Diabetes and Alzheimer\u2019s disease crosstalk","volume":"64","author":"Baglietto-Vargas","year":"2016","journal-title":"Neurosci. Biobehav. Rev."},{"key":"10.1016\/j.cmpb.2026.109609_bib0082","doi-asserted-by":"crossref","DOI":"10.1016\/j.arr.2024.102383","article-title":"True or false? Alzheimer\u2019s disease is type 3 diabetes: evidences from bench to bedside","volume":"99","author":"Peng","year":"2024","journal-title":"Ageing Res. Rev."},{"issue":"4","key":"10.1016\/j.cmpb.2026.109609_bib0083","doi-asserted-by":"crossref","first-page":"210","DOI":"10.2337\/ds16-0041","article-title":"Type 2 diabetes, cognition, and dementia in older adults: toward a precision health approach","volume":"29","author":"Cholerton","year":"2016","journal-title":"Diabetes Spect."},{"key":"10.1016\/j.cmpb.2026.109609_bib0084","doi-asserted-by":"crossref","first-page":"473","DOI":"10.3390\/biomedicines13020473","article-title":"A narrative review on cognitive impairment in type 2 diabetes: global trends and diagnostic approaches","volume":"13","author":"Liao","year":"2025","journal-title":"Biomedicines"},{"issue":"2","key":"10.1016\/j.cmpb.2026.109609_bib0085","doi-asserted-by":"crossref","first-page":"92","DOI":"10.4239\/wjd.v14.i2.92","article-title":"Diabetes and cognitive function: an evidence-based current perspective","volume":"14","author":"Sebastian","year":"2023","journal-title":"World J. Diabetes"},{"issue":"5","key":"10.1016\/j.cmpb.2026.109609_bib0086","doi-asserted-by":"crossref","first-page":"721","DOI":"10.3390\/electronics11050721","article-title":"Automated detection of Alzheimer\u2019s via hybrid classical quantum neural networks","volume":"11","author":"Shahwar","year":"2022","journal-title":"Electronics"},{"key":"10.1016\/j.cmpb.2026.109609_bib0087","series-title":"Proceedings of the IEEE MIT Undergraduate Research Technology Conference (URTC)","article-title":"Implementing a hybrid quantum-classical neural network by utilizing a variational quantum circuit for detection of dementia","author":"Kim","year":"2023"},{"key":"10.1016\/j.cmpb.2026.109609_bib0088","unstructured":"Chen K.C., Li Y.T., Li T.Y., Liu C.Y., Li P.H., Chen C.Y. CompressedMediQ: hybrid quantum machine learning pipeline for high-dimensional neuroimaging data [Internet]. 2024 Sep 21. Available from: http:\/\/arxiv.org\/abs\/2409.08584."},{"key":"10.1016\/j.cmpb.2026.109609_bib0089","article-title":"Early-stage detection of cognitive impairment by hybrid quantum-classical algorithm using resting-state functional MRI time-series","author":"Choi","year":"2024","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.cmpb.2026.109609_bib0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130565","article-title":"Quantum AI for Alzheimer's disease early screening","volume":"647","author":"Cappiello","year":"2025","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109609_bib0091","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-024-61452-1","article-title":"Deep ensemble learning and quantum machine learning approach for Alzheimer\u2019s disease detection","volume":"14","author":"Jenber Belay","year":"2024","journal-title":"Sci. Rep."},{"issue":"9","key":"10.1016\/j.cmpb.2026.109609_bib0092","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0331870","article-title":"CQ-CNN: a lightweight hybrid classical\u2013quantum convolutional neural network for Alzheimer's disease detection using 3D structural brain MRI","volume":"20","author":"Islam","year":"2025","journal-title":"PLOS One"},{"key":"10.1016\/j.cmpb.2026.109609_bib0093","series-title":"AIM-FM Workshop","article-title":"QViSTA: a novel quantum vision transformer for early multi-stage Alzheimer's diagnosis using optimized variational quantum circuits","author":"Singhal","year":"2024"},{"key":"10.1016\/j.cmpb.2026.109609_bib0094","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.jbi.2018.04.007","article-title":"Benchmarking deep learning models on large healthcare datasets","volume":"83","author":"Purushotham","year":"2018","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.cmpb.2026.109609_bib0095","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1038\/s41746-025-01597-z","article-title":"A systematic review of quantum machine learning for digital health","volume":"8","author":"Gupta","year":"2025","journal-title":"npj Digit. Med."},{"key":"10.1016\/j.cmpb.2026.109609_bib0096","series-title":"Ethics and Governance of Artificial Intelligence For Health: WHO Guidance","year":"2021"},{"issue":"1","key":"10.1016\/j.cmpb.2026.109609_bib0097","doi-asserted-by":"crossref","DOI":"10.1038\/s41746-024-01232-3","article-title":"Navigating the EU AI act: implications for regulated digital medical products","volume":"7","author":"Aboy","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"10.1016\/j.cmpb.2026.109609_bib0098","series-title":"Research Handbook on Health, AI and the Law","first-page":"422","author":"Barry","year":"2024"},{"key":"10.1016\/j.cmpb.2026.109609_bib0099","doi-asserted-by":"crossref","first-page":"79","DOI":"10.22331\/q-2018-08-06-79","article-title":"Quantum computing in the NISQ era and beyond","volume":"2","author":"Preskill","year":"2018","journal-title":"Quantum"},{"issue":"7965","key":"10.1016\/j.cmpb.2026.109609_bib0100","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41586-023-06096-3","article-title":"Evidence for the utility of quantum computing before fault tolerance","volume":"618","author":"Kim","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.cmpb.2026.109609_bib0101","doi-asserted-by":"crossref","first-page":"2631","DOI":"10.1038\/s41467-021-22539-9","article-title":"Power of data in quantum machine learning","volume":"12","author":"Huang","year":"2021","journal-title":"Nat. Commun."},{"issue":"5","key":"10.1016\/j.cmpb.2026.109609_bib0102","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3549524","article-title":"Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning","volume":"69","author":"Chia","year":"2022","journal-title":"J. ACM"},{"key":"10.1016\/j.cmpb.2026.109609_bib0103","series-title":"Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (STOC 2019)","first-page":"217","article-title":"A quantum-inspired classical algorithm for recommendation systems","author":"Tang","year":"2019"},{"key":"10.1016\/j.cmpb.2026.109609_bib0104","article-title":"A review on selective in-memory computing processors: potential alternative to AI-driven applications","volume":"29","author":"Mohith","year":"2026","journal-title":"Results Eng."},{"issue":"6124","key":"10.1016\/j.cmpb.2026.109609_bib0105","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1126\/science.1231930","article-title":"Superconducting circuits for quantum information: an outlook","volume":"339","author":"Devoret","year":"2013","journal-title":"Science"},{"key":"10.1016\/j.cmpb.2026.109609_bib0106","doi-asserted-by":"crossref","DOI":"10.1002\/dmrr.70011","article-title":"Evaluating red blood cells\u2019 membrane fluidity in diabetes: insights, mechanisms, and future aspects","volume":"41","author":"Pitocco","year":"2025","journal-title":"Diabetes Metab. Res. Rev."},{"issue":"18","key":"10.1016\/j.cmpb.2026.109609_bib0107","doi-asserted-by":"crossref","first-page":"3931","DOI":"10.3390\/nu15183931","article-title":"Unraveling the gut microbiome\u2013diet connection: exploring the impact of digital precision and personalized nutrition on microbiota composition and host physiology","volume":"15","author":"Bianchetti","year":"2023","journal-title":"Nutrients"},{"key":"10.1016\/j.cmpb.2026.109609_bib0108","doi-asserted-by":"crossref","first-page":"3806","DOI":"10.3390\/nu16223806","article-title":"Unraveling the gut microbiota: implications for precision nutrition and personalized medicine","volume":"16","author":"Abeltino","year":"2024","journal-title":"Nutrients"},{"key":"10.1016\/j.cmpb.2026.109609_bib0109","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1038\/s41525-025-00537-w","article-title":"Quantum computing and the implementation of precision medicine","volume":"10","author":"Nassir","year":"2025","journal-title":"npj Genom. Med."},{"issue":"3","key":"10.1016\/j.cmpb.2026.109609_bib0110","doi-asserted-by":"crossref","DOI":"10.1007\/s11128-021-03029-9","article-title":"A hybrid classical-quantum approach for multi-class classification","volume":"20","author":"Chalumuri","year":"2021","journal-title":"Quant. Inf. Process."},{"key":"10.1016\/j.cmpb.2026.109609_bib0111","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1038\/s41534-021-00456-5","article-title":"Nearest centroid classification on a trapped ion quantum computer","volume":"7","author":"Johri","year":"2021","journal-title":"npj Quant. Inf."},{"key":"10.1016\/j.cmpb.2026.109609_bib0112","doi-asserted-by":"crossref","first-page":"2453","DOI":"10.1038\/s41598-022-24082-z","article-title":"Quantum machine learning with differential privacy","volume":"13","author":"Watkins","year":"2023","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.cmpb.2026.109609_bib0113","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/TFUZZ.2024.3419559","article-title":"Quantum fuzzy federated learning for privacy protection in intelligent information processing","volume":"33","author":"Qu","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"3","key":"10.1016\/j.cmpb.2026.109609_bib0114","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevA.86.032324","article-title":"Surface codes: towards practical large-scale quantum computation","volume":"86","author":"Fowler","year":"2012","journal-title":"Phys. Rev. A"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726003585?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726003585?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T17:02:04Z","timestamp":1788368524000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260726003585"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":114,"alternative-id":["S0169260726003585"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109609","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Quantum machine learning in diabetes and metabolism: Applications and translational challenges","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109609","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"109609"}}