Cracked tooth syndrome is a commonly encountered disease in dentistry, which is often accompanied by dramatic painful responses from occlusion and temperature stimulation. Current clinical diagnostic trials include traditional methods (such as occlusion test, probing, cold stimulation, etc.) and X-rays based medical imaging (periapical radiography (PR), cone-beam computed tomography (CBCT), etc.). However, these methods are strongly dependent on the experience of the clinicians, and some inconspicuous cracks are also extremely easy to be overlooked by visual observation, which will definitely affect the subsequent treatments. Inspired by the achievements of applying deep convolutional neural networks (CNNs) in crack detection in engineering, this article proposes an image-based crack detection method using a deep CNN classifier in combination with a sliding window algorithm. A CNN model is designed by modifying the size of the input layer and adding a fully connected layer with 2 units based on the ResNet50, and then, the proposed CNN is trained and validated with a self-prepared cracked tooth dataset including 20,000 images. By comparing validation accuracy under seven different learning rates, 10−5 is chosen as the best learning rate for the following testing process. The trained CNN is tested on 100 images with 1920 × 1080-pixel resolutions, which achieves an average accuracy of 90.39%. The results show that the proposed method can effectively detect cracks in images under various conditions (stained, overexplosion, images affected by other diseases). The proposed method in this article provides doctors with a more intelligent diagnostic solution, and it is not only suitable for optical photographs but also for automated diagnosis of other medical imaging images.

https://downloads.hindawi.com/journals/abb/2022/9333406.pdf

Automatic Detection of Cracks in Cracked Tooth Based on Binary Classification Convolutional Neural Networks

Abstract Tooth diseases including dental caries, periodontitis and cracks have been public health problems globally. How to detect them at the early stage and perform thorough diagnosis are critical for the treatment. The diseases can be viewed as defects from the perspective of non-destructive testing. Such a defect can affect the material properties (e.g., optical, chemical, mechanical, acoustic, density and dielectric properties). A non-destructive testing method is commonly developed to sense the change of one particular property. Microwave testing is one that is focused on the dielectric properties. In recent years, this technique has received increased attention in dentistry. Here, the dielectric properties of human teeth are presented first, and the measurement methods are addressed. Then, the research progress on the detection of teeth over the last decade is reviewed, identifying achievements and challenges. Finally, the research trends are outlined, including electromagnetic simulation, radio frequency identification and heating-based techniques.

Non-destructive testing of human teeth using microwaves: a state-of-the-art review

Current dental diagnostics are image based and cannot detect a structural microgap defect such as a crack in a tooth. Whether percussion diagnostics can effectively diagnose a microgap defect is unclear.The purpose of the present study was to determine from a large multicenter prospective clinical study whether quantitative percussion diagnostics (QPD) could detect structural damage in teeth and whether a probability of its presence could be provided.A nonrandomized prospective and multicenter clinical validation study with 224 participants was performed in 5 centers with 6 independent investigators. The study used QPD and the normal fit error to determine whether a microgap defect was present in a natural tooth. Teams 1 and 2 were blinded. Team 1 tested teeth scheduled for restoration with QPD, and Team 2 disassembled the teeth aided by a clinical microscope, transillumination, and a penetrant dye. Microgap defects were documented in written and video formats. Controls were participants without damaged teeth. The percussion response from each tooth was stored on a computer and analyzed. A total of 243 teeth were tested to provide approximately 95% power to test the performance goal of 70%, based on an assumed population overall agreement of 80%.Regardless of the collection method, tooth geometry, restoration material used, or restoration type, the data on detecting a microgap defect in a tooth were accurate. The data also reflected good sensitivity and specificity consistent with previously published clinical studies. The combined study data showed an overall agreement of 87.5% with a 95% confidence interval (84.2 to 90.3), beyond the 70% predetermined performance goal. The combined study data determined whether it was possible to predict the probability of a microgap defect.The results showed that the data on detecting microgap defects in a tooth site were consistently accurate and confirmed that QPD provided information to aid the clinician in treatment planning and early preventative treatment. QPD can also alert the clinician of probable diagnosed and undiagnosed structural problems via the use of a probability curve. 

An evaluation of quantitative percussion diagnostics for determining the probability of a microgap defect in restored and unrestored teeth: A prospective clinical study.

Abstract Background To evaluate the treatment outcomes of the cracked tooth restored using occlusal veneer. Methods Thirty-seven cracked tooth with normal pulp or reversible pulpitis were analyzed, and their demographic and clinical data and medical history were collected. Patients were followed-up to examine the thermal/biting sensitivity and clinical features. Results The maxillary first molar was the most prevalent (N = 17, 46%), and most patients could accurately locate the offending teeth (N = 32, 83.5%). The crack lines (CL) were divided into 5 types depending on tooth preparation. Within 16 months of followed-up, 2 out of 32 teeth developed pulp necrosis, resulting in a rate of 93.75%. A cracked tooth with probing depth &gt; 6mm, number of CL on the finish line &gt; 4, number of CL through preparation on finish line &gt; 2 and widened periodontal ligament (PL) of the apical area were inclined to pulp necrosis after restoring the occlusal veneer. After restoration, 45.71% of the patients felt relief from thermal pain after 1 week, 71.42% after 1 month, 88.57% after 3 months, 94.28% after 6 months, and 100% after 12 months, while biting pain was 54.28%, 77.14%, 94.28%, 97.14%, and 100% cases after 1 week, 1, 3, 6 and 12 months, respectively. Probing depth &gt; 3mm was related to thermal sensitivity, while teeth located in the mandible were correlated with biting sensitivity post-treatment. Widened PL of the apical area was related to thermal and biting sensitivity with 9–19 months followed-up period. Conclusions Occlusal veneer is a feasible and effective therapy for the protection of cracked tooth. Trial registration: The Research Ethics Committee approved this study (PKUSSIRB-202272008). While it was also registered on the webpage of Chinese Clinical Trial Registry and approved by their Institutional Review Board (ChiCTR2200057462, 13/03/2022). 

/pdf/survival-and-prognostic-factors-of-managing-vital-cracked-2ktprutv.pdf

Survival and prognostic factors of managing vital cracked teeth with occlusal veneer restoration: A 1-to 2 years prospective clinical study

https://doi.org/10.1016/j.jdsr.2023.05.001

Letter to the editor regarding “Diagnosis of cracked tooth: Clinical status and research progress”

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. 

/pdf/a-perspective-on-the-diagnosis-of-cracked-tooth-imaging-2813rxeo.pdf

A perspective on the diagnosis of cracked tooth: imaging modalities evolve to AI-based analysis

An adaptive heuristic algorithm with a collaborative search framework for multi-UAV inspection planning

A Survey of Intelligent Optimization Algorithms for Unmanned Aerial Vehicle-Assisted Mobile Edge Computing

Piranha Predation Optimization Algorithm (PPOA) for Global Optimization and Engineering Design Problems

Altruistic nurturing algorithm for multi-objective autonomous underwater vehicles path planning problems

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Haibing Ouyang

Author Tools

Chat about Author