{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T06:27:00Z","timestamp":1786602420571,"version":"build-2736575974"},"reference-count":99,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>Chronic Kidney Disease (CKD) has emerged as a major public health concern worldwide, and most patients with CKD are asymptomatic until the later stages, causing growing morbidity and mortality. Diabetes and hypertension are the main causative factors for the development of CKD, damaging the renal microcirculation system. In addition, the impact of Acute Kidney Injuries (AKI) may result in the recovery or progression to either CKD or renal failure. The conventional techniques for diagnosis, such as the measurement of blood creatinine levels, are invasive and time-consuming and may also overlook the early stages of CKD. The non-invasive technology for the diagnosis of CKD has experienced tremendous improvements with the development in the field of sensing technology and the revolution in the field of Artificial Intelligence. This review article covers the non-invasive sensing technology using the non-invasive biofluids\/biological matrices, such as saliva, breath, and sweat, for the diagnosis of Chronic Kidney Disease. Additionally, the framework for incorporating Machine Learning models for the automated prediction of CKD in its early stages is analysed.<\/jats:p>","DOI":"10.3389\/frai.2026.1836646","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T05:32:13Z","timestamp":1786599133000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Recent advancements and future prospects on AI-integrated sensing techniques for non-invasive chronic kidney disease diagnosis: a review"],"prefix":"10.3389","volume":"9","author":[{"given":"Suchetha","family":"Manikandan","sequence":"first","affiliation":[{"name":"Centre for Healthcare Advancement, Innovation and Research, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Preethi","family":"Senthilkumar","sequence":"additional","affiliation":[{"name":"Centre for Healthcare Advancement, Innovation and Research, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shivani","family":"Selvachandran","sequence":"additional","affiliation":[{"name":"Malla Reddy Institute of Medical Sciences","place":["Hyderabad, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jim Elliot","family":"Christopherjames","sequence":"additional","affiliation":[{"name":"Centre for Healthcare Advancement, Innovation and Research, Vellore Institute of Technology","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shabbir","family":"Syed Abdul","sequence":"additional","affiliation":[{"name":"Graduate Institute of Biomedical Informatics, Taipei Medical University","place":["Taipei, Taiwan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1016\/J.COMPBIOMED.2017.09.017","article-title":"Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals","volume":"100","author":"Acharya","year":"2018","journal-title":"Comput. 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