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Although neural networks have proven effective for modeling complex patterns, they often function as black\u2010box systems, making their predictions difficult to interpret and limiting their clinical utility. To address these challenges, we propose the adaptive spatial key\u2010region identification (ASKRI) framework\u2014a novel method to identify region of interest, which combines adaptive sampling based on Shannon entropy, probability\u2010mean\u2010driven selection, and spatial uncertainty quantified via kriging method. ASKRI integrates block\u2010to\u2010block kriging with statistical inference to interpolate CNN\u2010derived classification performance, significantly reducing the computational burden of exhaustive model training without sacrificing predictive accuracy. Designed for seamless integration with convolutional neural networks (CNNs), ASKRI enhances both the accuracy and interpretability of ROI identification. Its effectiveness is demonstrated using the traumatic brain injury (TRACK\u2010TBI) dataset, where ASKRI reliably identifies spatially consistent and biologically meaningful regions associated with aging. These results underscore the framework's potential to advance brain image analysis, while offering transparent and resource\u2010efficient diagnostic support in clinical settings.<\/jats:p>","DOI":"10.1002\/sim.70653","type":"journal-article","created":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T15:34:29Z","timestamp":1782228869000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging\u2010Enhanced Adaptive Sampling"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-5130-0289","authenticated-orcid":false,"given":"HyunAh","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Biostatistics The State University of New York, University at Buffalo  Buffalo New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6866-5928","authenticated-orcid":false,"given":"Jihnhee","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics The State University of New York, University at Buffalo  Buffalo New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-0321","authenticated-orcid":false,"given":"Soyun","family":"Park","sequence":"additional","affiliation":[{"name":"Global Biometrics and Data Science Bristol Myers Squibb  Lawrence Township New Jersey USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zohi","family":"Sternberg","sequence":"additional","affiliation":[{"name":"Department of Neurology The State University of New York, University at Buffalo  Buffalo New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"key":"e_1_2_12_2_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190183"},{"key":"e_1_2_12_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.021"},{"key":"e_1_2_12_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.004"},{"issue":"1","key":"e_1_2_12_5_1","first-page":"1","article-title":"Machine Learning in Neuroimaging: From Research to Clinical Practice","volume":"62","author":"Nenning K. 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