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Stage 1 employed YOLOv8 to detect teeth #37 and #47. Stage 2 applied three CNNs (DenseNet-169, EfficientNet-B6, ConvNeXt-Base) and ensemble approaches to classify C-shaped canals on YOLO-derived crops. Data were split 70\/15\/15 for training\/validation\/testing. Detection was evaluated using IoU and mAP; classification via accuracy, sensitivity, specificity, F1-score, and AUC. Pairwise AUC comparisons used DeLong test with Bonferroni correction. Grad-CAM provided visual explanations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>YOLOv8 achieved high localization performance, reliably supplying regions of interest to classifiers. ConvNeXt-Base demonstrated the most consistent discrimination with top-tier accuracy and F1-score. A binary EfficientNet-ConvNeXt ensemble performed comparably to the best individual model, while the tertiary ensemble showed no improvement. DeLong analysis revealed statistically significant AUC advantage of ConvNeXt-Base over DenseNet-169 for tooth #47 after correction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>This two-stage pipeline enables accurate, automation-ready detection of C-shaped canals from routine panoramic radiographs, potentially informing patient triage and CBCT indication decisions. Multicenter validation is warranted before clinical deployment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical relevance<\/jats:title>\n                    <jats:p>This AI system offers a practical screening tool using existing panoramic radiographs to identify high-risk patients, potentially improving treatment planning efficiency, reducing unnecessary CBCT referrals, and alerting clinicians to complex anatomy before endodontic therapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00784-026-06868-x","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T02:56:43Z","timestamp":1776913003000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automated detection of C-shaped canals in mandibular second molars from panoramic radiographs: comparing single and ensemble convolutional neural networks within a 2-stage pipeline"],"prefix":"10.1007","volume":"30","author":[{"given":"Yunus Emre","family":"\u00c7akmak","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K\u00fcr\u015fat","family":"Er","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"key":"6868_CR1","doi-asserted-by":"crossref","unstructured":"\u00d8rstavik D (ed) (2020) Essential endodontology: prevention and treatment of apical periodontitis, 3rd edn. 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