SBIR-STTR Award

Epidemiologic studies report that cracked teeth are the third most common cause for tooth loss in industrialized countries. All cracks are colonized by bacteria, which have the potential to cause pulpal and periapical disease. Pain associated with these diseases is intense and is the most common reason for seeking emergency dental care. Cracked teeth present a complex, diagnostic dilemma since symptoms associated with cracked teeth are often discontinuous with periods of remission. If left undetected, cracks continue to progress and ultimately result in tooth loss. Available clinical tools used to detect cracks have limitations and often cannot determine the extent of the crack. To address this we propose to develop an open-source software solution based on 3D Isotropic Steerable Wavelets that will accurately detect the presence and quantify cracks in any location and orientation. The work proposed will a) create a novel image analysis method and software solution for 3D tracing and 3D visualization of tooth cracks using high resolution CBCT and b) perform validation of the 3D crack detection biomarkers on extracted human teeth. The work proposed addresses a significant need for quantitative, reproducible and evidence-based techniques to detect and localize cracks on teeth. Successful completion of this Phase I proposal will result in a Phase II SBIR proposal focused on the clinical validation of the proposed methodology on different types of cracks in-vivo. This will ultimately lead to a better understanding of this pathology and thus to better preventive strategies and improved tracking of the progression of cracks in patients.

Public Health Relevance Statement:
PROJECT NARRATIVE Diagnosing cracked teeth is difficult with current diagnostic tools, and undetected cracks ultimately result in tooth loss. We will develop a software solution based on 3D Isotropic Steerable Wavelets that will accurately detect the presence of and quantify cracks in any location and orientation. This proposal addresses a significant need for quantitative, reproducible and evidence-based techniques to detect and localize cracks on teeth. The outcome will be better preventive strategies and an improved tracking of the progression of cracks. Ultimately this will lead to targeted interventions and better clinical outcomes.

Project Terms:
Address; associated symptom; Bacteria; base; Biological Markers; Clinical; cohort; Complex; Computer software; cone-beam computed tomography; Data; Dental; Dental Care; Detection; Developed Countries; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Diagnostic radiologic examination; Digital Signal Processing; Disease; Disease remission; Dolphins; Dose; Emergency Situation; epidemiology study; evidence base; experience; Fracture; Frequencies; Generations; Goals; Gold; Gray unit of radiation dose; histological studies; Human; Image; Image Analysis; Imagery; imaging biomarker; imaging modality; improved; in vivo; innovation; Intervention; Lead; Left; Length; Location; Mathematics; Measurement; Measures; Methodology; Methods; Modality; Morphology; novel; open source; Outcome; Pain; pain reduction; Pathology; Patients; Performance; Periapical Diseases; Phase; Prevention strategy; Protocols documentation; quantitative imaging; Radiation; Reporting; Reproducibility; Resolution; response; Signal Transduction; Small Business Innovation Research Grant; software development; Space Perception; Symptoms; Techniques; Technology; Tomography, Computed, Scanners; tool; Tooth Diseases; Tooth Loss; Tooth structure; treatment choice; Validation; Vendor; Work

Epidemiological studies indicate that cracked teeth are the third most common cause of tooth loss inindustrialized countries. Histological studies demonstrate that all cracks are colonized by bacteria, which have the potential to cause intensely painful pulpal and periapical infections. The early detection of cracks (incompletefractures) followed by appropriate interventions to prevent crack propagation are effective strategies to prevent infections and avert tooth loss. Current tools used to diagnose cracks are inadequate and there is an imperativeneed to develop an objective and reliable method to detect cracks. During our Phase I project, we developed and tested a novel algorithm for crack detection on extracted human teeth. Using machine learning and imaging features extracted from three-dimensional (3D) wavelets, we demonstrated enhanced crack detection hr-CBCT.We now propose to further refine this technology and to validate it clinically. Our hypothesis is that our method increases the predictive validity of hr-CBCT in detecting cracks. This development will happen with close clinical guidance. Also, we will collaborate with CBCT hardware vendors to increase the impact of our commercializationplan. This proposal addresses the need for quantitative, reproducible, and evidence-based ways to detectcracks in teeth, that can potentially lead to improved tooth loss prevention.

Public Health Relevance Statement:
PROJECT NARRATIVE Cracked teeth are a common cause of tooth loss in industrialized countries. Histological studies demonstrate that all cracks are colonized by bacteria, which have the potential to cause painful pulpal and periapical infections. The early detection of cracks followed by appropriate interventions are effective strategies to prevent infections and avert tooth loss. Current tools used to diagnose cracks are inadequate and there is an imperative need to develop an objective and reliable method to detect cracks. This Phase II proposal will refine the pilot algorithm for crack detection we developed during Phase I, and it will validate it clinically. This proposal addresses the need for quantitative, reproducible, and evidence-based ways to detect cracks in teeth, that can potentially prevent tooth loss.

Project Terms:
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