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In survival analysis, recent advances of DNNs primarily focus on enhancing model capabilities, especially in exploring nonlinear covariate effects under right censoring. However, deep learning methods for interval\u2010censored data, where the unobservable failure time is only known to lie in an interval, remain underexplored and limited to specific data types or models. This work proposes a general regression framework for interval\u2010censored data with a broad class of partially linear transformation models, where key covariate effects are modeled parametrically while nonlinear effects of nuisance covariates are approximated via DNNs, balancing interpretability and flexibility. We employ sieve maximum likelihood estimation by leveraging monotone splines to approximate the cumulative baseline hazard function. To ensure reliable and tractable estimation, we develop an EM algorithm incorporating stochastic gradient descent. We establish the asymptotic properties of parameter estimators and show that the DNN estimator achieves minimax\u2010optimal convergence. Extensive simulations demonstrate superior estimation and prediction accuracy over state\u2010of\u2010the\u2010art methods. Applying our method to the Alzheimer's Disease Neuroimaging Initiative dataset yields novel insights and improved predictive performance compared to traditional approaches.<\/jats:p>","DOI":"10.1002\/sim.70609","type":"journal-article","created":{"date-parts":[[2026,5,25]],"date-time":"2026-05-25T11:35:42Z","timestamp":1779708942000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable Deep Regression Models With Interval\u2010Censored Failure Time Data"],"prefix":"10.1002","volume":"45","author":[{"given":"Changhui","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Mathematics Jilin University  Changchun China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shishun","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Mathematics Jilin University  Changchun China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Economics and Statistics Guangzhou University  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4877-3200","authenticated-orcid":false,"given":"Xinyuan","family":"Song","sequence":"additional","affiliation":[{"name":"Department of Statistics Chinese University of Hong Kong  Hong Kong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Data Science Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"The Alzheimer's Disease Neuroimaging Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,25]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/21681163.2015.1131197"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomtc\/ujad024"},{"key":"e_1_2_12_4_1","unstructured":"J.Devlin M. 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