Improvement of region of interest extraction and scanning method of computer-aided diagnosis system for osteoporosis using panoramic radiographs

Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review

BACKGROUND

Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities.

STUDY DESIGN

A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another.

RESULTS

The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection.

CONCLUSION

Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.

Harnessing the Power of Artificial Intelligence in Cleft Lip and Palate: An In-Depth Analysis from Diagnosis to Treatment, a Comprehensive Review

Cleft lip and palate (CLP) is the most common craniofacial malformation, with a range of physical, psychological, and aesthetic consequences. In this comprehensive review, our main objective is to thoroughly examine the relationship between CLP anomalies and the use of artificial intelligence (AI) in children. Additionally, we aim to explore how the integration of AI technology can bring about significant advancements in the fields of diagnosis, treatment methods, and predictive outcomes. By analyzing the existing evidence, we will highlight state-of-the-art algorithms and predictive AI models that play a crucial role in achieving precise diagnosis, susceptibility assessment, and treatment planning for children with CLP anomalies. Our focus will specifically be on the efficacy of alveolar bone graft and orthodontic interventions. The findings of this review showed that deep learning (DL) models revolutionize the diagnostic process, predict susceptibility to CLP, and enhance alveolar bone grafts and orthodontic treatment. DL models surpass human capabilities in terms of precision, and AI algorithms applied to large datasets can uncover the intricate genetic and environmental factors contributing to CLP. Additionally, Machine learning aids in preoperative planning for alveolar bone grafts and provides personalized treatment plans in orthodontic treatment. In conclusion, these advancements inspire optimism for a future where AI seamlessly integrates with CLP management, augmenting its analytical capabilities.

Osteoporosis screening support system from panoramic radiographs using deep learning by convolutional neural network

OBJECTIVES

This study was performed to develop computer-aided screening systems that could predict osteoporosis. The systems were constructed using panoramic radiographs of women aged ≥ 50 years through three types of deep convolutional neural networks (CNNs): Alexnet, VGG-16, and GoogLeNet; the performances of the constructed systems were evaluated.

METHODS

One oral radiologist classified 1500 panoramic radiographs into three types. In C1, the endosteal margin of the cortex was smooth and sharp, whereas porosities were observed in C2 and C3. The risks of osteoporosis were higher in C2 and C3 than in C1; C3 had the highest risk. This information was included with the images as training data; three CNNs were transfer trained. Using each trained CNN, the diagnostic accuracy was assessed using panoramic radiographs and bone mineral density inspection findings in the lumbar spine and femoral neck of 100 additional patients.

RESULTS

All CNNs exhibited relatively good agreement with the oral radiologist's judgement (86.0%-90.7%). The predictive results of the three systems for osteoporosis of the lumbar spine showed sensitivities of 78.3%-82.6%, specificities of 71.4%-79.2%, and accuracies of 74.0%-79.0%. The predictive results for osteoporosis of the femoral neck showed sensitivities of 80.0%-86.7%, specificities of 67.1%-74.1%, and accuracies of 70.0%-75.0%.

CONCLUSIONS

The constructed systems were generally more accurate than the previously developed conventional system. The new systems may facilitate osteoporosis prediction and prevent subsequent fractures by encouraging patients with suspected osteoporosis to undergo further inspections (e.g., dual-energy X-ray absorptiometry) and treatment.

AI-based computer-aided diagnosis for panoramic radiographs: Quantitative analysis of mandibular cortical morphology in relation to age and gender

OBJECTIVE

This study aimed to investigate AI-based computer-aided diagnosis (AI-CAD) for panoramic radiographs, especially quantitative evaluation of mandibular cortical morphology in relation to age and gender.

METHODS

321 patients with jaw lesions who underwent panoramic radiography were prospectively included. The mandibular cortical morphology was analyzed with an AI-CAD that evaluated the degree of deformation of mandibular inferior cortex and mandibular cortical index (MCI) automatically. Those were analyzed in relation to age and gender, such as younger (≦ 20 years), middle (21-60 years) and older group (≧ 61 years) in men and women.

RESULTS

The degree of deformation in older men (33.0 ± 18.5) was higher than those of middle (25.0 ± 15.3, p = 0.030) and younger (32.5 ± 16.9, p = 0.993), and those in older women (46.2 ± 22.5) was higher than those of middle (19.4 ± 16.5, p < 0.001) and younger (22.4 ± 14.5, p < 0.001). The MCI of women was a significant difference for aging (p < 0.001), although those of men was not significant difference for aging (p = 0.189).

CONCLUSION

The AI-CAD could be a useful tool for the quantitative analysis of mandibular cortical morphology.

Detection and classification of unilateral cleft alveolus with and without cleft palate on panoramic radiographs using a deep learning system

Although panoramic radiography has a role in the examination of patients with cleft alveolus (CA), its appearances is sometimes difficult to interpret. The aims of this study were to develop a computer-aided diagnosis system for diagnosing the CA status on panoramic radiographs using a deep learning object detection technique with and without normal data in the learning process, to verify its performance in comparison to human observers, and to clarify some characteristic appearances probably related to the performance. The panoramic radiographs of 383 CA patients with cleft palate (CA with CP) or without cleft palate (CA only) and 210 patients without CA (normal) were used to create two models on the DetectNet. The models 1 and 2 were developed based on the data without and with normal subjects, respectively, to detect the CAs and classify them into with or without CP. The model 2 reduced the false positive rate (1/30) compared to the model 1 (12/30). The overall accuracy of Model 2 was higher than Model 1 and human observers. The model created in this study appeared to have the potential to detect and classify CAs on panoramic radiographs, and might be useful to assist the human observers.

An investigation of tooth loss factors in elderly patients using panoramic radiographs

OBJECTIVES

The aim of this study was to observe the dental condition in a group of elderly patients over a period of 10 years in order to clarify important risk factors.

MATERIALS AND METHODS

Participants were elderly patients (in their eighties) who took panoramic radiographs between 2015 and 2016, and for whom panoramic radiographs taken around 10 year earlier were also available. The number of remaining and lost teeth, the Eichner Index, the presence or absence of molar occlusion, the respective condition of dental pulp, dental crowns, alveolar bone resorption, as well as periapical lesions were investigated through the analysis of panoramic radiographs. Additionally, other important variables were collected from patients' medical records. From the obtained panoramic radiograph sets, the patients' dental condition was investigated, and a systematic comparison was conducted.

RESULTS

The analysis of the panoramic radiographs showed that the number of remaining teeth decreased from an average of 20.8-15.5, and the percentage of patients with 20 or more teeth decreased from 69.2 to 26.9%. A factor analysis investigating tooth loss risk suggested that tooth loss was associated with the bridge, P2 or greater resorption of the alveolar bone, and apical lesions, and gender (with males having a higher risk compared to females).

CONCLUSIONS

Teeth showing P2 or greater alveolar bone resorption, bridge, and apical lesions on panoramic radiographs are most likely to be lost in an elderly patient's near future. Consequently, this group should be encouraged to visit their dental clinics regularly and receive comprehensive instruction on individual self-care methods.

Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey

Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract.

Computer-aided diagnosis system for osteoporosis based on quantitative evaluation of mandibular lower border porosity using panoramic radiographs

OBJECTIVES

A new computer-aided screening system for osteoporosis using panoramic radiographs was developed. The conventional system could detect porotic changes within the lower border of the mandible, but its severity could not be evaluated. Our aim was to enable the system to measure severity by implementing a linear bone resorption severity index (BRSI) based on the cortical bone shape.

METHODS

The participants were 68 females (>50 years) who underwent panoramic radiography and lumbar spine bone density measurements. The new system was designed to extract the lower border of the mandible as region of interests and convert them into morphological skeleton line images. The total perimeter length of the skeleton lines was defined as the BRSI. 40 images were visually evaluated for the presence of cortical bone porosity. The correlation between visual evaluation and BRSI of the participants, and the optimal threshold value of BRSI for new system were investigated through a receiver operator characteristic analysis. The diagnostic performance of the new system was evaluated by comparing the results from new system and lumbar bone density tests using 28 participants.

RESULTS

BRSI and lumbar bone density showed a strong negative correlation (p < 0.01). BRSI showed a strong correlation with visual evaluation. The new system showed high diagnostic efficacy with sensitivity of 90.9%, specificity of 64.7%, and accuracy of 75.0%.

CONCLUSIONS

The new screening system is able to quantitatively evaluate mandibular cortical porosity. This allows for preventive screening for osteoporosis thereby enhancing clinical prospects.

Computer programme to assess mandibular cortex morphology in cases of medication-related osteonecrosis of the jaw with osteoporosis or bone metastases

Purpose

The purpose of this study was to evaluate the morphology of the mandibular cortex in cases of medication-related osteonecrosis of the jaw (MRONJ) in patients with osteoporosis or bone metastases using a computer programme.

Materials and Methods

Fifty-four patients with MRONJ (35 with osteoporosis and 19 with bone metastases) were examined using panoramic radiography. The morphology of the mandibular cortex was evaluated using a computer programme that scanned the mandibular inferior cortex and automatically assessed the mandibular cortical index (MCI) according to the thickness and roughness of the mandibular cortex, as follows: normal (class 1), mildly to moderately eroded (class 2), or severely eroded (class 3). The MCI classifications of MRONJ patients with osteoporosis or bone metastases were evaluated with the Pearson chi-square test. In these analyses, a 5% significance level was used.

Results

The MCI of MRONJ patients with osteoporosis (class 1: 6, class 2: 15, class 3: 14) tended to be higher than that of patients with bone metastases (class 1: 14, class 2: 5, class 3: 0) (P=0.000).

Conclusion

The use of a computer programme to assess mandibular cortex morphology may be an effective technique for the objective and quantitative evaluation of the MCI in MRONJ patients with osteoporosis or bone metastases.