ReviewBiomedical engineering online2022
A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis.
Review in Biomedical engineering online, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 23 citations in OpenAlex.
- Deep learning-driven super-resolution for cone-beam computed tomography: AnImaging science in dentistry · 2026Article
- Diagnostic performance of swept-source optical coherence tomography in the detection of tooth cracks: a narrative review.Acta odontologica Scandinavica · 2026Review
- Saving the split: An umbrella review on therapeutic approaches for cracked tooth syndrome.Journal of conservative dentistry and endodontics · 2026Review
- Evaluation of the Role of Artificial Intelligence-Assisted Diagnosis of Early-Stage Oral Cancer: A Clinical Study.Journal of pharmacy & bioallied sciences · 2025Article
- A comparative analysis of deep learning models for assisting in the diagnosis of periapical lesions in periapical radiographs.BMC oral health · 2025Article
- Periodontitis bone loss detection in panoramic radiographs using modified YOLOv7.PeerJ. Computer science · 2025Article
- Article
- Letter to the editor regarding "Diagnosis of cracked tooth: Clinical status and research progress".The Japanese dental science review · 2023Article
- Automatic Detection of Cracks in Cracked Tooth Based on Binary Classification Convolutional Neural Networks.Applied bionics and biomechanics · 2022Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Despite numerous clinical trials and pre-clinical developments, the diagnosis of cracked tooth, especially in the early stages, remains a challenge. Cracked tooth syndrome is often accompanied by dramatic painful responses from occlusion and temperature stimulation, which has become one of the leading causes for tooth loss in adults. Current clinical diagnostical approaches for cracked tooth have been widely investigated based on X-rays, optical light, ultrasound wave, etc. Advances in artificial intelligence (AI) development have unlocked the possibility of detecting the crack in a more intellectual and automotive way. This may lead to the possibility of further enhancement of the diagnostic accuracy for cracked tooth disease. In this review, various medical imaging technologies for diagnosing cracked tooth are overviewed. In particular, the imaging modality, effect and the advantages of each diagnostic technique are discussed. What's more, AI-based crack detection and classification methods, especially the convolutional neural network (CNN)-based algorithms, including image classification (AlexNet), object detection (YOLO, Faster-RCNN), semantic segmentation (U-Net, Segnet) are comprehensively reviewed. Finally, the future perspectives and challenges in the diagnosis of the cracked tooth are lighted.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.