Detection of Trabecular Landmarks for Osteoporosis Prescreening in Dental Panoramic Radiographs

Diagnostic accuracy of artificial intelligence models in detecting osteoporosis using dental images: a systematic review and meta-analysis

The current study aimed to systematically review the literature on the accuracy of artificial intelligence (AI) models for osteoporosis (OP) diagnosis using dental images. A thorough literature search was executed in October 2022 and updated in November 2023 across multiple databases, including PubMed, Scopus, Web of Science, and Google Scholar. The research targeted studies using AI models for OP diagnosis from dental radiographs. The main outcomes were the sensitivity and specificity of AI models regarding OP diagnosis. The "meta" package from the R Foundation was selected for statistical analysis. A random-effects model, along with 95% confidence intervals, was utilized to estimate pooled values. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was employed for risk of bias and applicability assessment. Among 640 records, 22 studies were included in the qualitative analysis and 12 in the meta-analysis. The overall sensitivity for AI-assisted OP diagnosis was 0.85 (95% CI, 0.70-0.93), while the pooled specificity equaled 0.95 (95% CI, 0.91-0.97). Conventional algorithms led to a pooled sensitivity of 0.82 (95% CI, 0.57-0.94) and a pooled specificity of 0.96 (95% CI, 0.93-0.97). Deep convolutional neural networks exhibited a pooled sensitivity of 0.87 (95% CI, 0.68-0.95) and a pooled specificity of 0.92 (95% CI, 0.83-0.96). This systematic review corroborates the accuracy of AI in OP diagnosis using dental images. Future research should expand sample sizes in test and training datasets and standardize imaging techniques to establish the reliability of AI-assisted methods in OP diagnosis through dental images.

Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies.

Opportunistic Osteoporosis Screening Using Chest Radiographs With Deep Learning: Development and External Validation With a Cohort Dataset

Osteoporosis is a common, but silent disease until it is complicated by fractures that are associated with morbidity and mortality. Over the past few years, although deep learning-based disease diagnosis on chest radiographs has yielded promising results, osteoporosis screening remains unexplored. Paired data with 13,026 chest radiographs and dual-energy X-ray absorptiometry (DXA) results from the Health Screening and Promotion Center of Asan Medical Center, between 2012 and 2019, were used as the primary dataset in this study. For the external test, we additionally used the Asan osteoporosis cohort dataset (1089 chest radiographs, 2010 and 2017). Using a well-performed deep learning model, we trained the OsPor-screen model with labels defined by DXA based diagnosis of osteoporosis (lumbar spine, femoral neck, or total hip T-score ≤ -2.5) in a supervised learning manner. The OsPor-screen model was assessed in the internal and external test sets. We performed substudies for evaluating the effect of various anatomical subregions and image sizes of input images. OsPor-screen model performances including sensitivity, specificity, and area under the curve (AUC) were measured in the internal and external test sets. In addition, visual explanations of the model to predict each class were expressed in gradient-weighted class activation maps (Grad-CAMs). The OsPor-screen model showed promising performances. Osteoporosis screening with the OsPor-screen model achieved an AUC of 0.91 (95% confidence interval [CI], 0.90-0.92) and an AUC of 0.88 (95% CI, 0.85-0.90) in the internal and external test set, respectively. Even though the medical relevance of these average Grad-CAMs is unclear, these results suggest that a deep learning-based model using chest radiographs could have the potential to be used for opportunistic automated screening of patients with osteoporosis in clinical settings. © 2021 American Society for Bone and Mineral Research (ASBMR).

Statistical Shape and Appearance Models: Development Towards Improved Osteoporosis Care

PURPOSE OF REVIEW

Statistical models of shape and appearance have increased their popularity since the 1990s and are today highly prevalent in the field of medical image analysis. In this article, we review the recent literature about how statistical models have been applied in the context of osteoporosis and fracture risk estimation.

RECENT FINDINGS

Recent developments have increased their ability to accurately segment bones, as well as to perform 3D reconstruction and classify bone anatomies, all features of high interest in the field of osteoporosis and fragility fractures diagnosis, prevention, and treatment. An increasing number of studies used statistical models to estimate fracture risk in retrospective case-control cohorts, which is a promising step towards future clinical application. All the reviewed application areas made considerable steps forward in the past 5-6 years. Heterogeneities in validation hinder a thorough comparison between the different methods and represent one of the future challenges to be addressed to reach clinical implementation.

Osteoporosis Prescreening and Bone Mineral Density Prediction using Dental Panoramic Radiographs

Recent studies have shown that Dental Panoramic Radiograph (DPR) images have great potential for prescreening of osteoporosis given the high degree of correlation between the bone density and trabecular bone structure. Most of the research works in these area had used pretrained models for feature extraction and classification with good success. However, when the size of the data set is limited it becomes difficult to use these pretrained networks and gain high confidence scores. In this paper, we evaluated the diagnostic performance of deep convolutional neural networks (DCNN)based computer-assisted diagnosis (CAD) system in the detection of osteoporosis on panoramic radiographs, through a comparison with diagnoses made by oral and maxillofacial radiologists. With the available labelled dataset of 70 images, results were reproduced for the preliminary study model. Furthermore, the model performance was enhanced using different computer vision techniques. Specifically, the age meta data available for each patient was leveraged to obtain more accurate predictions. Lastly, we tried to leverage these images, ages and osteoporotic labels to create a neural network based regression model and predict the Bone Mineral Density (BMD) value for each patient. Experimental results showed that the proposed CAD system was in high accord with experienced oral and maxillofacial radiologists in detecting osteoporosis and achieved 87.86% accuracy.Clinical relevance- This paper presents a method to detect osteoporosis using DPR images and age data with multi-column DCNN and then leverage this data to predict Bone Mineral Density for each patient.

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy.

Clinical guidelines for the application of panoramic radiographs in screening for osteoporosis

Osteoporotic fractures are associated with an increased risk of subsequent fractures, a higher rate of mortality, and incremental medical costs. Incidental findings, which include some measurements related to the mandibular inferior cortex and the alveolar trabecular bone pattern of the mandible determined on panoramic radiographs, are considered to be a useful tool for identifying asymptomatic individuals at risk of having osteoporosis and/or fragility fractures. We undertook a worldwide literature survey and present the following clinical recommendations. Postmenopausal female dental patients with a mandibular inferior cortical width of less than 3 mm on panoramic radiographs may be at risk of having low skeletal bone mineral density (BMD) or osteoporosis, but not fragility fractures. In addition, those with a severely eroded mandibular inferior cortex may have an increased risk of having low skeletal BMD, osteoporosis, and fragility fractures. The alveolar trabecular bone pattern of the mandible might be useful for identifying female dental patients at risk of having fragility fractures, although further investigation is necessary to confirm this possibility. These incidental findings on panoramic radiographs, when used for identifying asymptomatic postmenopausal female patients at risk of having osteoporosis in general dental practice, may be helpful in reducing the incidence of first fractures, with a consequent reduction in the secondary fractures, medical costs, and mortality associated with osteoporotic fragility fractures, without incurring any additional cost.