{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T05:21:08Z","timestamp":1788153668961,"version":"build-2803163510"},"reference-count":46,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T00:00:00Z","timestamp":1787961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009096","name":"Firat University Scientific Research Projects Unit","doi-asserted-by":"publisher","award":["DHF.25.14"],"award-info":[{"award-number":["DHF.25.14"]}],"id":[{"id":"10.13039\/100009096","id-type":"DOI","asserted-by":"publisher"}]},{"award":["DHF.25.14"],"award-info":[{"award-number":["DHF.25.14"]}],"id":[{"id":"https:\/\/ror.org\/05teb7b63","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Apical periodontitis is among the most prevalent dental pathologies, and its radiographic severity is graded on panoramic radiographs using the ordinal periapical index (PAI 3 &lt; 4 &lt; 5). Existing computer-aided systems typically treat these grades as unrelated classes and down-sample the radiograph to a fixed input, which erases the small periradicular radiolucencies that define early disease. This study evaluated a lesion-preserving, ordinal-aware YOLO framework for the simultaneous detection and severity grading of periapical lesions. Methods: A total of 3924 expert-annotated panoramic radiographs (PAI 3\/4\/5) were processed with an adaptive lesion-preserving tiling algorithm that mapped each radiograph onto native-resolution 640 \u00d7 640 tiles so that every lesion appeared intact within exactly one tile, yielding 4641 tiles. Six consecutive YOLO generations (YOLOv8, YOLOv9, YOLOv10, YOLO11, YOLO12, and YOLO26) were benchmarked under one identical pipeline on a locked hold-out test set of 985 lesions, of which 628 were PAI 3, 274 PAI 4, and 83 PAI 5. YOLO12m, which attained the highest point estimates and the smallest run-to-run variation, was then progressively augmented with an ordinal-aware detection loss, a high-resolution P2 head, severity class-weighting and test-time augmentation. Localization was measured with COCO mAP and severity grading with severity-weighted average precision (Sev-wAP), quadratic weighted kappa (QWK), a catastrophic off-by-two error rate, and per-grade recall. Results: Lesion-preserving tiling improved detection for every generation, raising the mAP@0.50 of YOLO12m from 0.494 to 0.615, the highest point estimate among the six generations, although the margin over the next two was within the resolution of this test set. Cumulatively adding the ordinal loss, the P2 head, severity weighting, and test-time augmentation raised mAP@0.50 to 0.664, mAP@0.50:0.95 to 0.333, Sev-wAP to 0.704, and QWK to 0.780. PAI 5 average precision rose from 0.268 to 0.331 and PAI 5 recall from 0.602 to 0.663, 55 of 83 severe lesions, while catastrophic confusion between mild and severe lesions stood at 0.41%, 4 of the 985 reference lesions. Conclusions: Coupling lesion-preserving tiling with ordinal severity-aware detection improves localization and severity-weighted precision by resolvable margins, shifts every severe-grade measure in a clinically preferable direction, and provides a reproducible reference protocol for comparing detector architectures on this task.<\/jats:p>","DOI":"10.3390\/diagnostics16172778","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T04:24:03Z","timestamp":1788150243000},"page":"2778","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated Detection and Ordinal Severity Grading of Periapical Lesions on Panoramic Radiographs Using a Lesion-Preserving Tiling and Ordinal-Aware YOLO Framework"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5131-0063","authenticated-orcid":false,"given":"Faruk","family":"Oztekin","sequence":"first","affiliation":[{"name":"Department of Endodontics, Faculty of Dentistry, Firat University, Elazig 23119, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5628-3543","authenticated-orcid":false,"given":"Oguzhan","family":"Katar","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, Firat University, Elazig 23119, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nurefsan","family":"Avci","sequence":"additional","affiliation":[{"name":"Department of Endodontics, Faculty of Dentistry, Firat University, Elazig 23119, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1107-4337","authenticated-orcid":false,"given":"Furkan","family":"Konus","sequence":"additional","affiliation":[{"name":"Department of Endodontics, Faculty of Dentistry, Yozgat Bozok University, Yozgat 66100, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Niazi, S.A., and Bakhsh, A. (2022). Association between Endodontic Infection, Its Treatment and Systemic Health: A Narrative Review. Medicina, 58.","DOI":"10.3390\/medicina58070931"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1111\/iej.13787","article-title":"Effectiveness of Root Canal Filling Materials and Techniques for Treatment of Apical Periodontitis: A Systematic Review","volume":"56","author":"Pirani","year":"2023","journal-title":"Int. Endod. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1111\/iej.13897","article-title":"Factors That Affect the Outcomes of Root Canal Treatment and Retreatment\u2014A Reframing of the Principles","volume":"56","author":"Gulabivala","year":"2023","journal-title":"Int. Endod. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"97","DOI":"10.5577\/intdentres.2022.vol12.no2.8","article-title":"Indexes for Periapical Health Evaluation: A Review","volume":"12","author":"Rajasekhar","year":"2022","journal-title":"Int. Dent. Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1111\/j.1600-9657.1986.tb00119.x","article-title":"The Periapical Index: A Scoring System for Radiographic Assessment of Apical Periodontitis","volume":"2","author":"Kerekes","year":"1986","journal-title":"Dent. Traumatol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3125","DOI":"10.1007\/s10278-024-01360-y","article-title":"Development of Periapical Index Score Classification System in Periapical Radiographs Using Deep Learning","volume":"38","author":"Hirata","year":"2025","journal-title":"J. Imaging Inform. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s44445-025-00060-9","article-title":"Current Prevalence and Risk Factors of Apical Periodontitis in Healthy Saudi Adults Attending Dental School at Najran University: A Cross-Sectional Study Using Cone Beam Computed Tomography","volume":"37","author":"Alhomrany","year":"2025","journal-title":"Saudi Dent. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gontarz, M., Wieczorkiewicz, A., Hramyka, A., Bargiel, J., G\u0105siorowski, K., Szczurowski, P., Nelke, K., Czopik, B., Uranbey, \u00d6., and Rusek, K. (2026). Prognostic Factors for Adverse Outcomes in Odontogenic Infections Requiring Hospitalization: A Single-Center Retrospective Study in Krak\u00f3w, Poland. J. Clin. Med., 15.","DOI":"10.3390\/jcm15135120"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Sunde, P.T., Giving, E., Dilshad, T., Handal, T., and \u00d8rstavik, D. (2025). Radiographic Outcome of Endodontic Treatment of Teeth with Primary Apical Periodontitis: Results from a Postgraduate Clinic. Dent. J., 13.","DOI":"10.20944\/preprints202510.1358.v1"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Thurzo, A., and Thurzo, V. (2025). Embedding Fear in Medical AI: A Risk-Averse Framework for Safety and Ethics. AI, 6.","DOI":"10.3390\/ai6050101"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1093\/rpd\/ncab069","article-title":"Effective Doses and Radiation Risks from Common Dental Radiographic, Panoramic and CBCT Examinations","volume":"195","author":"Shatskiy","year":"2021","journal-title":"Radiat. Prot. Dosim."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109574","DOI":"10.1016\/j.identj.2026.109574","article-title":"Image Resolution\u2019s Impact on Artificial Intelligence & Human Accuracy in Full-Mouth Radiographic Analysis","volume":"76","author":"Mayer","year":"2026","journal-title":"Int. Dent. J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Obuchowicz, B., Zarzecka, J., Jakubowska, M., Obuchowicz, R., Strzelecki, M., Pi\u00f3rkowski, A., Go\u0142da, J., Nurzynska, K., and Lasek, J. (2026). Radiomics as a Decision Support Tool for Detecting Occult Periapical Lesions on Intraoral Radiographs. J. Clin. Med., 15.","DOI":"10.3390\/jcm15030971"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Aloufi, A.S. (2026). Detection of Periapical Lesions Using Artificial Intelligence: A Narrative Review. Diagnostics, 16.","DOI":"10.3390\/diagnostics16020301"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Lutescu, D.A., Manole, C.C., \u021a\u00e2ncu, A.M.C., Ilinca, R., Chau, R.C.W., Felszeghy, S., and Didilescu, A.C. (2026). Can Artificial Intelligence in Orthopantomography Advance Dental Diagnostics through Automated Image Analysis?. Front. Radiol., 6.","DOI":"10.3389\/fradi.2026.1701356"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27\u201330 June 2016, IEEE.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"680","DOI":"10.1111\/iej.13265","article-title":"Evaluation of Artificial Intelligence for Detecting Periapical Pathosis on Cone-beam Computed Tomography Scans","volume":"53","author":"Orhan","year":"2020","journal-title":"Int. Endod. J."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1016\/j.joen.2020.03.025","article-title":"Artificial Intelligence for the Computer-Aided Detection of Periapical Lesions in Cone-Beam Computed Tomographic Images","volume":"46","author":"Setzer","year":"2020","journal-title":"J. Endod."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108527","DOI":"10.1016\/j.compbiomed.2024.108527","article-title":"Artificial Intelligence vs. Semi-Automated Segmentation for Assessment of Dental Periapical Lesion Volume Index Score: A Cone-Beam CT Study","volume":"175","author":"Boubaris","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105854","DOI":"10.1016\/j.jdent.2025.105854","article-title":"Diagnostic Accuracy of an Artificial Intelligence-Based Platform in Detecting Periapical Radiolucencies on Cone-Beam Computed Tomography Scans of Molars","volume":"160","author":"Allihaibi","year":"2025","journal-title":"J. Dent."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Akalin, F., and \u00d6zkan, Y. (2025). Deep Learning-Based Hybrid Scenario for Classification of Periapical Lesions in Cone Beam Computed Tomography. Symmetry, 17.","DOI":"10.3390\/sym17091392"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1016\/j.joen.2019.03.016","article-title":"Deep Learning for the Radiographic Detection of Apical Lesions","volume":"45","author":"Ekert","year":"2019","journal-title":"J. Endod."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Endres, M.G., Hillen, F., Salloumis, M., Sedaghat, A.R., Niehues, S.M., Quatela, O., Hanken, H., Smeets, R., Beck-Broichsitter, B., and Rendenbach, C. (2020). Development of a Deep Learning Algorithm for Periapical Disease Detection in Dental Radiographs. Diagnostics, 10.","DOI":"10.3390\/diagnostics10060430"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"20230118","DOI":"10.1259\/dmfr.20230118","article-title":"The Role of Deep Learning for Periapical Lesion Detection on Panoramic Radiographs","volume":"52","author":"Kaya","year":"2023","journal-title":"Dentomaxillofac. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, C.-W., Lin, S.-Y., Chou, H.-S., Chen, T.-Y., Chen, Y.-A., Liu, S.-Y., Liu, Y.-L., Chen, C.-A., Huang, Y.-C., and Chen, S.-L. (2021). Detection of Dental Apical Lesions Using CNNs on Periapical Radiograph. Sensors, 21.","DOI":"10.3390\/s21217049"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Szab\u00f3, V., Orhan, K., Dob\u00f3-Nagy, C., Veres, D.S., Manulis, D., Ezhov, M., Sanders, A., and Szab\u00f3, B.T. (2025). Deep Learning-Based Periapical Lesion Detection on Panoramic Radiographs. Diagnostics, 15.","DOI":"10.3390\/diagnostics15040510"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ba-Hattab, R., Barhom, N., Osman, S.A.A., Naceur, I., Odeh, A., Asad, A., Al-Najdi, S.A.R.N., Ameri, E., Daer, A., and Silva, R.L.B.D. (2023). Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning. Appl. Sci., 13.","DOI":"10.3390\/app13031516"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Scarano, G., Agostinelli, S., Amerini, I., and Papi, P. (2026). YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs. J. Imaging, 12.","DOI":"10.3390\/jimaging12060272"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Fatima, A., Shafi, I., Afzal, H., Mahmood, K., D\u00edez, I.D.L.T., Lipari, V., Brito Ballester, J., and Ashraf, I. (2023). Deep Learning-Based Multiclass Instance Segmentation for Dental Lesion Detection. Healthcare, 11.","DOI":"10.3390\/healthcare11030347"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1007\/s00784-021-04043-y","article-title":"Deep Learning for Categorization of Endodontic Lesion Based on Radiographic Periapical Index Scoring System","volume":"26","author":"Moidu","year":"2022","journal-title":"Clin. Oral Investig."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Lee, J., Park, Y., Kim, S., and Jang, I. (2026). Mamba-Based Deep Learning Model for Automated Periapical Index Classification Using Periapical Radiographs and Clinical Metadata. Int. Endod. J., Online ahead of print.","DOI":"10.1111\/iej.70170"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"493","DOI":"10.1007\/s11282-024-00759-1","article-title":"Comparing the Accuracy of Two Machine Learning Models in Detection and Classification of Periapical Lesions Using Periapical Radiographs","volume":"40","author":"Viet","year":"2024","journal-title":"Oral Radiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"36487","DOI":"10.1038\/s41598-025-21761-5","article-title":"Automated Assessment of Periapical Health Based on the Radiographic Periapical Index Using YOLOv8, YOLOv11, and YOLOv12 One-Stage Object Detection Algorithms","volume":"15","author":"Saber","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Erkal, D., \u00c7akmak, Y.E., Er, K., and Ku\u015ftarc\u0131, A. (2026). Automated Periapical Lesion Segmentation and Area-Based PAI Indexing: A Comparative Deep Learning Study on Periapical Radiographs. BMC Oral Health, 26.","DOI":"10.1186\/s12903-026-08645-4"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"110486","DOI":"10.1016\/j.dib.2024.110486","article-title":"A Dataset of Apical Periodontitis Lesions in Panoramic Radiographs for Deep-Learning-Based Classification and Detection","volume":"54","author":"Do","year":"2024","journal-title":"Data Brief"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft Coco: Common Objects in Context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland, 6\u201312 September 2014, Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_37","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2023). Ultralytics YOLOv8, Ultralytics. Available online: https:\/\/docs.ultralytics.com\/models\/yolov8\/."},{"key":"ref_38","unstructured":"Jocher, G., Qiu, J., Liu, M., Lyu, S., Akyon, F.C., and Kalfaoglu, M.E. (2026). Ultralytics YOLO26: Unified Real-Time End-to-End Vision Models. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y.M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. Proceedings of the European Conference on Computer Vision (ECCV), Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. Advances in Neural Information Processing Systems (NeurIPS), Curran Associates Inc.","DOI":"10.52202\/079017-3429"},{"key":"ref_41","unstructured":"Jocher, G., and Qiu, J. (2024). Ultralytics YOLO11, Ultralytics. Available online: https:\/\/docs.ultralytics.com\/models\/yolo11\/."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). YOLOv12: Attention-Centric Real-Time Object Detectors. Advances in Neural Information Processing Systems (NeurIPS), Curran Associates Inc.","DOI":"10.52202\/085713-2627"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Fang, X., Cai, J., Liu, H., Zhou, C., Lu, M., and Chen, B. (2025). PerioDet: Large-Scale Panoramic Radiograph Benchmark for Clinical-Oriented Apical Periodontitis Detection. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Daejeon, Republic of Korea, 23\u201327 September 2025, Springer.","DOI":"10.1007\/978-3-032-05325-1_40"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Mure\u0219anu, S., Hede\u0219iu, M., Iacob, L., Eftimie, R., Olariu, E., Dinu, C., Jacobs, R., and Group, T.P. (2024). Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities. Diagnostics, 14.","DOI":"10.3390\/diagnostics14202336"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Ramien, G.N., Jaeger, P.F., Kohl, S.A.A., and Maier-Hein, K.H. (2019). Reg R-CNN: Lesion Detection and Grading under Noisy Labels. Proceedings of the Workshop on Clinical Image-Based Procedures, Shenzhen, China, 17 October 2019, Springer.","DOI":"10.1007\/978-3-030-32689-0_4"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1007\/s44411-025-00063-2","article-title":"How Is AI Transforming Medical Research, Education and Practice?","volume":"126","author":"Thurzo","year":"2025","journal-title":"Bratisl. Med. J."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/17\/2778\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T04:53:34Z","timestamp":1788152014000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/17\/2778"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,29]]},"references-count":46,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16172778"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16172778","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,29]]}}}