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While deep learning (DL) algorithms have shown promise in objectifying ABR detection in controlled settings, their applicability to real-world clinical data is hindered by small datasets and insufficient heterogeneity. This study evaluates the generalizability of nine DL models for ABR detection using large, multicenter datasets. The primary dataset analyzed, Clinical Dataset I, comprises 128,123 labeled ABRs from 13,813 participants across a wide range of ages and hearing levels, and was divided into a training set (90%) and a held-out test set (10%). The models included convolutional neural networks (CNNs; AlexNet, VGG, ResNet), transformer-based architectures (Transformer, Patch Time Series Transformer [PatchTST], Differential Transformer, and Differential PatchTST), and hybrid CNN-transformer models (ResTransformer, ResPatchTST). Performance was assessed on the held-out test set and four external datasets (Clinical II, Southampton, PhysioNet, Mendeley) using accuracy and area under the receiver operating characteristic curve (AUC). ResPatchTST achieved the highest performance on the held-out test set (accuracy: 91.90%, AUC: 0.976). Transformer-based models, particularly PatchTST, showed superior generalization to external datasets, maintaining robust accuracy across diverse clinical settings. Additional experiments highlighted the critical role of dataset size and diversity in enhancing model robustness. We also observed that incorporating acquisition parameters and demographic features as auxiliary inputs yielded performance gains in cross-center generalization. These findings underscore the potential of DL models\u2014especially transformer-based architectures\u2014for accurate and generalizable ABR detection, and highlight the necessity of large, diverse datasets in developing clinically reliable systems.<\/jats:p>","DOI":"10.1177\/23312165251347773","type":"journal-article","created":{"date-parts":[[2025,6,3]],"date-time":"2025-06-03T03:18:32Z","timestamp":1748920712000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":5,"title":["Comparison of Deep Learning Models for Objective Auditory Brainstem Response Detection: A Multicenter Validation Study"],"prefix":"10.1177","volume":"29","author":[{"given":"Yin","family":"Liu","sequence":"first","affiliation":[{"name":"Chongqing University of Posts and Telecommunications"},{"name":"Chongqing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingjie","family":"Xiang","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Li","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kangkang","family":"Li","sequence":"additional","affiliation":[{"name":"Tianyue Xinchuang Information Technology Corporation, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yihan","family":"Yang","sequence":"additional","affiliation":[{"name":"Peking University First Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiantian","family":"Wang","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuting","family":"Qin","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxing","family":"Fu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Otolaryngology, Beijing Tongren Hospital, Capital Medical University, Beijing, China"},{"name":"Ear Science Institute Australia"},{"name":"The University of Western Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenqiang","family":"Gao","sequence":"additional","affiliation":[{"name":"Chongqing University of Posts and Telecommunications"},{"name":"School of Intelligent Systems Engineering, the Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University, Shenzhen, Guangdong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,6,3]]},"reference":[{"key":"e_1_3_3_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2005.08.004"},{"key":"e_1_3_3_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fnhum.2023.1077409"},{"key":"e_1_3_3_4_1","volume-title":"Classification of auditory brainstem responses by human experts and backipropagation neural networksPaper presented at the Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society","volume":"13","author":"Alpsan D.","year":"1991","unstructured":"Alpsan D. 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