Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis

Abesi, Farida; Maleki, Mahla; Zamani, Mohammad

doi:10.5624/isd.20220224

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…To date, few meta-analyses and systematic reviews have been conducted on the utilization of AI for PL detection [58][59][60][61][62][63][64]. An important study by Silva et al [60] showed that the pooled diagnostic accuracy of CBCT for PL detection was 88.75% (95% confidence interval = 85.…”

Section: Discussionmentioning

confidence: 99%

Periapical Lesions in Panoramic Radiography and CBCT Imaging—Assessment of AI’s Diagnostic Accuracy

Kazimierczak,

Wajer,

Wajer

et al. 2024

JCM

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Background/Objectives: Periapical lesions (PLs) are frequently detected in dental radiology. Accurate diagnosis of these lesions is essential for proper treatment planning. Imaging techniques such as orthopantomogram (OPG) and cone-beam CT (CBCT) imaging are used to identify PLs. The aim of this study was to assess the diagnostic accuracy of artificial intelligence (AI) software Diagnocat for PL detection in OPG and CBCT images. Methods: The study included 49 patients, totaling 1223 teeth. Both OPG and CBCT images were analyzed by AI software and by three experienced clinicians. All the images were obtained in one patient cohort, and findings were compared to the consensus of human readers using CBCT. The AI’s diagnostic accuracy was compared to a reference method, calculating sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and F1 score. Results: The AI’s sensitivity for OPG images was 33.33% with an F1 score of 32.73%. For CBCT images, the AI’s sensitivity was 77.78% with an F1 score of 84.00%. The AI’s specificity was over 98% for both OPG and CBCT images. Conclusions: The AI demonstrated high sensitivity and high specificity in detecting PLs in CBCT images but lower sensitivity in OPG images.

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Section: Discussionmentioning

confidence: 99%

Periapical Lesions in Panoramic Radiography and CBCT Imaging—Assessment of AI’s Diagnostic Accuracy

Kazimierczak,

Wajer,

Wajer

et al. 2024

JCM

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show abstract

“…Several systematic reviews and meta-analyses have been conducted on the utilization of AI for identifying caries and periapical lucencies [44][45][46][47][48][49][50][51][52][53][54][55]. In a recent comprehensive study by Rahimi [54], the accuracy of classification models for caries detection was evaluated across 48 studies.…”

Section: Dental Diagnosticsmentioning

confidence: 99%

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review

Kazimierczak,

Serafin

et al. 2024

JCM

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The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.

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“…Cone beam computed tomography (CBCT) is a tool that provides three-dimensional volumetric data on maxillofacial structures and the assessment of airway area and volume [ 9 ]. The inclusion of AI for 3D image analysis enables the prediction of risk factors involved in diseases such as obstructive sleep apnea (OSA) syndrome [ 10 ], as well as speeding up the diagnosis and improving the data interpretation process [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

Ravelo,

Acero,

Fuentes-Zambrano

et al. 2024

JPM

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AI is included in a lot of different systems. In facial surgery, there are some AI-based software programs oriented to diagnosis in facial surgery. This study aims to evaluate the capacity and training of models for diagnosis of dentofacial deformities in class II and class III patients using artificial intelligence and the potential use for indicating orthognathic surgery. The search strategy is from 1943 to April 2024 in PubMed, Embase, Scopus, Lilacs, and Web of Science. Studies that used imaging to assess anatomical structures, airway volume, and craniofacial positions using the AI algorithm in the human population were included. The methodological quality of the studies was assessed using the Effective Public Health Practice Project instrument. The systematic search identified 697 articles. Eight studies were obtained for descriptive analysis after exclusion according to our inclusion and exclusion criteria. All studies were retrospective in design. A total of 5552 subjects with an age range between 14.7 and 56 years were obtained; 2474 (44.56%) subjects were male, and 3078 (55.43%) were female. Six studies were analyzed using 2D imaging and obtained highly accurate results in diagnosing skeletal features and determining the need for orthognathic surgery, and two studies used 3D imaging for measurement and diagnosis. Limitations of the studies such as age, diagnosis in facial deformity, and the included variables were observed. Concerning the overall analysis bias, six studies were at moderate risk due to weak study designs, while two were at high risk of bias. We can conclude that, with the few articles included, using AI-based software allows for some craniometric recognition and measurements to determine the diagnosis of facial deformities using mainly 2D analysis. However, it is necessary to perform studies based on three-dimensional images, increase the sample size, and train models in different populations to ensure accuracy of AI applications in this field. After that, the models can be trained for dentofacial diagnosis

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Diagnostic performance of artificial intelligence using cone-beam computed tomography imaging of the oral and maxillofacial region: A scoping review and meta-analysis

Cited by 5 publications

References 39 publications

Periapical Lesions in Panoramic Radiography and CBCT Imaging—Assessment of AI’s Diagnostic Accuracy

Periapical Lesions in Panoramic Radiography and CBCT Imaging—Assessment of AI’s Diagnostic Accuracy

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning—A Comprehensive Review

Artificial Intelligence Used for Diagnosis in Facial Deformities: A Systematic Review

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