Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs.

OBJECTIVES: This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs. METHODS: The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independent observers using an AI-based (Diagnocat) system. The presence or absence of carious lesions was recorded during Phase 1. After 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiographic data (Phase 3). Subsequently, data reflecting human disagreements were excluded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of Diagnocat, were calculated. RESULTS: During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were κ=0.66-1, κ=0.58-0.7, and κ=0.49-0.7. The Fleiss kappa values were κ=0.57-0.8. The sensitivity, specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively. CONCLUSIONS: The Diagnocat CNN supports the evaluation of intraoral radiographs for caries diagnosis, as determined by consensus between human and AI system observers. CLINICAL SIGNIFICANCE: Our study may aid in the understanding of deep learning-based systems developed for dental imaging modalities for dentists and contribute to expanding the body of results in the field of AI-supported dental radiology..

Evaluation of a Decision Support System Developed with Deep Learning Approach for Detecting Dental Caries with Cone-Beam Computed Tomography Imaging.

This study aims to investigate the effect of using an artificial intelligence (AI) system (Diagnocat, Inc., San Francisco, CA, USA) for caries detection by comparing cone-beam computed tomography (CBCT) evaluation results with and without the software. 500 CBCT volumes are scored by three dentomaxillofacial radiologists for the presence of caries separately on a five-point confidence scale without and with the aid of the AI system. After visual evaluation, the deep convolutional neural network (CNN) model generated a radiological report and observers scored again using AI interface. The ground truth was determined by a hybrid approach. Intra- and inter-observer agreements are evaluated with sensitivity, specificity, accuracy, and kappa statistics. A total of 6008 surfaces are determined as 'presence of caries' and 13,928 surfaces are determined as 'absence of caries' for ground truth. The area under the ROC curve of observer 1, 2, and 3 are found to be 0.855/0.920, 0.863/0.917, and 0.747/0.903, respectively (unaided/aided). Fleiss Kappa coefficients are changed from 0.325 to 0.468, and the best accuracy (0.939) is achieved with the aided results. The radiographic evaluations performed with aid of the AI system are found to be more compatible and accurate than unaided evaluations in the detection of dental caries with CBCT images.

Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs.

Purpose: The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs (PRs), as well as to assess the appropriateness of its treatment recommendations. Material and Methods: PRs from 100 patients (representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. Results: The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Conclusion: Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.

Assessing the reliability of CBCT-based AI-generated STL files in diagnosing osseous changes of the mandibular condyle: a comparative study with ground truth diagnosis.

OBJECTIVES: This study aims to evaluate the reliability of AI-generated STL files in diagnosing osseous changes of the mandibular condyle and compare them to a ground truth (GT) diagnosis made by six radiologists. METHODS: A total of 432 retrospective CBCT images from four universities were evaluated by six dentomaxillofacial radiologists who identified osseous changes such as flattening, erosion, osteophyte formation, bifid condyle formation, and osteosclerosis. All images were evaluated by each radiologist blindly and recorded on a spreadsheet. All evaluations were compared and for the disagreements, a consensus meeting was held online to create a uniform GT diagnosis spreadsheet. A web-based dental AI software was used to generate STL files of the CBCT images, which were then evaluated by two dentomaxillofacial radiologists. The new observer, GT, was compared to this new STL file evaluation, and the interclass correlation (ICC) value was calculated for each pathology. RESULTS: Out of the 864 condyles assessed, the ground truth diagnosis identified 372 cases of flattening, 185 cases of erosion, 70 cases of osteophyte formation, 117 cases of osteosclerosis, and 15 cases of bifid condyle formation. The ICC values for flattening, erosion, osteophyte formation, osteosclerosis, and bifid condyle formation were 1.000, 0.782, 1.000, 0.000, and 1.000, respectively, when comparing diagnoses made using STL files with the ground truth. CONCLUSIONS: AI-generated STL files are reliable in diagnosing bifid condyle formation, osteophyte formation, and flattening of the condyle. However, the diagnosis of osteosclerosis using AI-generated STL files is not reliable, and the accuracy of diagnosis is affected by the erosion grade.

AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients.

This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a measurement method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network-based machine learning algorithm was used for the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate, and severe groups, and the mean airway volumes of the groups were compared. The narrowest points of the airway (mm), the field of the airway (mm2), and volume of the airway (cc) of both OSA and non-OSA patients were also compared. There was no statistically significant difference between the manual technique and Diagnocat measurements in all groups (p > 0.05). Inter-class correlation coefficients were 0.954 for manual and automatic segmentation, 0.956 for Diagnocat and automatic segmentation, 0.972 for Diagnocat and manual segmentation. Although there was no statistically significant difference in total airway volume measurements between the manual measurements, automatic measurements, and DC measurements in non-OSA and OSA patients, we evaluated the output images to understand why the mean value for the total airway was higher in DC measurement. It was seen that the DC algorithm also measures the epiglottis volume and the posterior nasal aperture volume due to the low soft-tissue contrast in CBCT images and that leads to higher values in airway volume measurement.

Clinically applicable artificial intelligence system for dental diagnosis with CBCT.

In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting. The system consists of 5 modules: ROI-localization-module (segmentation of teeth and jaws), tooth-localization and numeration-module, periodontitis-module, caries-localization-module, and periapical-lesion-localization-module. These modules use CNN based on state-of-the-art architectures. In total, 1346 CBCT scans were used to train the modules. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. 24 dentists participated in the clinical evaluation of the system. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. The sensitivity values for aided and unaided groups were 0.8537 and 0.7672 while specificity was 0.9672 and 0.9616 respectively. There was a statistically significant difference between the groups (p = 0.032). This study showed that the proposed AI system significantly improved the diagnostic capabilities of dentists.

A deep learning approach for dental implant planning in cone-beam computed tomography images.

BACKGROUND: The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images. METHODS: Seventy-five CBCT images were included in this study. In these images, bone height and thickness in 508 regions where implants were required were measured by a human observer with manual assessment method using InvivoDental 6.0 (Anatomage Inc. San Jose, CA, USA). Also, canals/sinuses/fossae associated with alveolar bones and missing tooth regions were detected. Following, all evaluations were repeated using the deep convolutional neural network (Diagnocat, Inc., San Francisco, USA) The jaws were separated as mandible/maxilla and each jaw was grouped as anterior/premolar/molar teeth region. The data obtained from manual assessment and AI methods were compared using Bland-Altman analysis and Wilcoxon signed rank test. RESULTS: In the bone height measurements, there were no statistically significant differences between AI and manual measurements in the premolar region of mandible and the premolar and molar regions of the maxilla (p > 0.05). In the bone thickness measurements, there were statistically significant differences between AI and manual measurements in all regions of maxilla and mandible (p < 0.001). Also, the percentage of right detection was 72.2% for canals, 66.4% for sinuses/fossae and 95.3% for missing tooth regions. CONCLUSIONS: Development of AI systems and their using in future for implant planning will both facilitate the work of physicians and will be a support mechanism in implantology practice to physicians.

Evaluation of artificial intelligence for detecting impacted third molars on cone-beam computed tomography scans.

PURPOSE: The aim of this study was to evaluate the diagnostic performance of artificial intelligence (AI) application evaluating of the impacted third molar teeth in Cone-beam Computed Tomography (CBCT) images. MATERIAL AND METHODS: In total, 130 third molar teeth (65 patients) were included in this retrospective study. Impaction detection, Impacted tooth numbers, root/canal numbers of teeth, relationship with adjacent anatomical structures (inferior alveolar canal and maxillary sinus) were compared between the human observer and AI application. Recorded parameters agreement between the human observer and AI application based on the deep-CNN system was evaluated using the Kappa analysis. RESULTS: In total, 112 teeth (86.2%) were detected as impacted by AI. The number of roots was correctly determined in 99 teeth (78.6%) and the number of canals in 82 teeth (68.1%). There was a good agreement in the determination of the inferior alveolar canal in relation to the mandibular impacted third molars (kappa: 0.762) as well as the number of roots detection (kappa: 0.620). Similarly, there was an excellent agreement in relation to maxillary impacted third molar and the maxillary sinus (kappa: 0.860). For the maxillary molar canal number detection, a moderate agreement was found between the human observer and AI examinations (kappa: 0.424). CONCLUSIONS: Artificial Intelligence (AI) application showed high accuracy values in the detection of impacted third molar teeth and their relationship to anatomical structures.