{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,18]],"date-time":"2026-10-18T02:42:58Z","timestamp":1792291378087,"version":"4.4.0"},"reference-count":58,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T00:00:00Z","timestamp":1777161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007120","name":"King Mongkut\u2019s Institute of Technology Ladkrabang","doi-asserted-by":"publisher","award":["2567-02-21-001"],"award-info":[{"award-number":["2567-02-21-001"]}],"id":[{"id":"10.13039\/501100007120","id-type":"DOI","asserted-by":"publisher"}]},{"award":["2567-02-21-001"],"award-info":[{"award-number":["2567-02-21-001"]}],"id":[{"id":"https:\/\/ror.org\/055mf0v62","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Monolithic zirconia restorations are frequently affected by the unnoticed growth of subcritical cracks, a failure process that is not captured by traditional imaging methods like radiographs and ultrasounds in sophisticated dental architectures. To address this evaluative inadequacy, this research introduces a hierarchical deep learning framework for microcrack detection and spatial localization. We promote a hierarchical deep learning system that integrates Acoustic Emission (AE) detection alongside signal processing. Raw AE signals utilized during dynamic loading are enhanced via Kalman filtering and Continuous Wavelet Transform (CWT) to construct high-fidelity time\u2013frequency scalograms. The diagnostic pipeline operates in two stages: first, a hybrid CNN\u2013BiGRU network with temporal attention fulfills zirconia component-level classification; second, a ResNet-18 backbone integrated with Bidirectional LSTM and Multi-Head Attention precisely localizes defects across five anatomical crown regions. This hierarchical design effectively captures the non-stationary, transient nature of fracture-induced stress waves. The framework achieved an F1-score of 99.00% and an AUC of 0.994, significantly outperforming conventional convolutional networks. By enabling predictive maintenance through early, non-invasive damage localization, this study demonstrates a promising laboratory framework for AE-based crack detection in zirconia dental structures and prosthetics and toward enhanced clinical reliability in digital dentistry.<\/jats:p>","DOI":"10.3390\/s26092682","type":"journal-article","created":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T08:12:31Z","timestamp":1777363951000},"page":"2682","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Smart Diagnostics: Hierarchical Deep Learning of Acoustic Emission Signals for Early Crack Detection in Zirconia Dental Structures"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1836-0247","authenticated-orcid":false,"given":"Kuson","family":"Tuntiwong","sequence":"first","affiliation":[{"name":"School of Dentistry, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rangsinee","family":"Wangman","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, School of Engineering, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kanchana","family":"Kanchanatawewat","sequence":"additional","affiliation":[{"name":"School of Dentistry, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boonjira","family":"Anucul","sequence":"additional","affiliation":[{"name":"School of Dentistry, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2617-647X","authenticated-orcid":false,"given":"Hiranya","family":"Sritart","sequence":"additional","affiliation":[{"name":"Department of Medical Technology, Faculty of Allied Health Sciences, Thammasat University, Pathumthani 12120, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2387-6059","authenticated-orcid":false,"given":"Pattarapong","family":"Phasukkit","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, School of Engineering, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3938-6057","authenticated-orcid":false,"given":"Supan","family":"Tungjitkusolmun","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, School of Engineering, King Mongkut\u2019s Institute of Technology Ladkrabang, Bangkok 10520, Thailand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alqutaibi, A.Y., Al-Sowygh, Z.H., Alotaibi, R.K., and Alharbi, F.M. 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