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Yet these questions remained largely unresolved due to the bottleneck of manual tissue inspection. Here, we developed an integrated artificial intelligence (AI)-guided imaging, processing, and statistical pipeline enabling unbiased, high-throughput analysis of more than 2 million candidate islets from 106 non-diabetic (ND) and T1D donors. We identified age-, region-, sex-, and islet size-dependent differences in islet distribution and composition between ND and T1D donors. Profound \u03b2-cell loss in T1D was accompanied by reciprocal \u03b1-cell expansion, whereas \u03b4-cells and pancreatic polypeptide cells were largely resilient. Cell area and pseudotime analyses uncovered regional and age-dependent trajectories of islet remodeling across T1D progression, along with distinct patterns of cytoarchitectural reorganization of the endocrine pancreas.<\/jats:p>","DOI":"10.64898\/2026.06.05.730081","type":"posted-content","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T01:10:22Z","timestamp":1781053822000},"source":"Crossref","is-referenced-by-count":0,"title":["AI-guided analysis of human pancreatic islet sociology reveals distinct cell compositional changes in type 1 diabetes"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7296-9732","authenticated-orcid":false,"given":"Caroline","family":"Ward","sequence":"first","affiliation":[{"name":"Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA"},{"name":"Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, 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