1
|
Kazimierczak W, Kazimierczak N, Issa J, Wajer R, Wajer A, Kalka S, Serafin Z. Endodontic Treatment Outcomes in Cone Beam Computed Tomography Images-Assessment of the Diagnostic Accuracy of AI. J Clin Med 2024; 13:4116. [PMID: 39064157 PMCID: PMC11278304 DOI: 10.3390/jcm13144116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 07/09/2024] [Accepted: 07/12/2024] [Indexed: 07/28/2024] Open
Abstract
Background/Objectives: The aim of this study was to assess the diagnostic accuracy of the AI-driven platform Diagnocat for evaluating endodontic treatment outcomes using cone beam computed tomography (CBCT) images. Methods: A total of 55 consecutive patients (15 males and 40 females, aged 12-70 years) referred for CBCT imaging were included. CBCT images were analyzed using Diagnocat's AI platform, which assessed parameters such as the probability of filling, adequate obturation, adequate density, overfilling, voids in filling, short filling, and root canal number. The images were also evaluated by two experienced human readers. Diagnostic accuracy metrics (accuracy, precision, recall, and F1 score) were assessed and compared to the readers' consensus, which served as the reference standard. Results: The AI platform demonstrated high diagnostic accuracy for most parameters, with perfect scores for the probability of filling (accuracy, precision, recall, F1 = 100%). Adequate obturation showed moderate performance (accuracy = 84.1%, precision = 66.7%, recall = 92.3%, and F1 = 77.4%). Adequate density (accuracy = 95.5%, precision, recall, and F1 = 97.2%), overfilling (accuracy = 95.5%, precision = 86.7%, recall = 100%, and F1 = 92.9%), and short fillings (accuracy = 95.5%, precision = 100%, recall = 86.7%, and F1 = 92.9%) also exhibited strong performance. The performance of AI for voids in filling detection (accuracy = 88.6%, precision = 88.9%, recall = 66.7%, and F1 = 76.2%) highlighted areas for improvement. Conclusions: The AI platform Diagnocat showed high diagnostic accuracy in evaluating endodontic treatment outcomes using CBCT images, indicating its potential as a valuable tool in dental radiology.
Collapse
Affiliation(s)
- Wojciech Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13–15, 85-067 Bydgoszcz, Poland
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Julien Issa
- Chair of Practical Clinical Dentistry, Department of Diagnostics, Poznań University of Medical Sciences, 61-701 Poznań, Poland
| | - Róża Wajer
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
| | - Adrian Wajer
- Dental Primus, Poznańska 18, 88-100 Inowrocław, Poland
| | - Sandra Kalka
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13–15, 85-067 Bydgoszcz, Poland
| |
Collapse
|
2
|
Kazimierczak W, Wajer R, Wajer A, Kalka K, Kazimierczak N, Serafin Z. Evaluating the Diagnostic Accuracy of an AI-Driven Platform for Assessing Endodontic Treatment Outcomes Using Panoramic Radiographs: A Preliminary Study. J Clin Med 2024; 13:3401. [PMID: 38929931 PMCID: PMC11203965 DOI: 10.3390/jcm13123401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2024] [Revised: 06/06/2024] [Accepted: 06/07/2024] [Indexed: 06/28/2024] Open
Abstract
Background/Objectives: The purpose of this preliminary study was to evaluate the diagnostic performance of an AI-driven platform, Diagnocat (Diagnocat Ltd., San Francisco, CA, USA), for assessing endodontic treatment outcomes using panoramic radiographs (PANs). Materials and Methods: The study included 55 PAN images of 55 patients (15 males and 40 females, aged 12-70) who underwent imaging at a private dental center. All images were acquired using a Hyperion X9 PRO digital cephalometer and were evaluated using Diagnocat, a cloud-based AI platform. The AI system assessed the following endodontic treatment features: filling probability, obturation adequacy, density, overfilling, voids in filling, and short filling. Two human observers independently evaluated the images, and their consensus served as the reference standard. The diagnostic accuracy metrics were calculated. Results: The AI system demonstrated high accuracy (90.72%) and a strong F1 score (95.12%) in detecting the probability of endodontic filling. However, the system showed variable performance in other categories, with lower accuracy metrics and unacceptable F1 scores for short filling and voids in filling assessments (8.33% and 14.29%, respectively). The accuracy for detecting adequate obturation and density was 55.81% and 62.79%, respectively. Conclusions: The AI-based system showed very high accuracy in identifying endodontically treated teeth but exhibited variable diagnostic accuracy for other qualitative features of endodontic treatment.
Collapse
Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland (N.K.)
| | - Róża Wajer
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
| | - Adrian Wajer
- Dental Primus, Poznańska 18, 88-100 Inowrocław, Poland
| | - Karol Kalka
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland (N.K.)
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland (N.K.)
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital No. 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
| |
Collapse
|
3
|
Kazimierczak W, Wajer R, Wajer A, Kiian V, Kloska A, Kazimierczak N, Janiszewska-Olszowska J, Serafin Z. Periapical Lesions in Panoramic Radiography and CBCT Imaging-Assessment of AI's Diagnostic Accuracy. J Clin Med 2024; 13:2709. [PMID: 38731237 PMCID: PMC11084607 DOI: 10.3390/jcm13092709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 04/30/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital no 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Róża Wajer
- Department of Radiology and Diagnostic Imaging, University Hospital no 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
| | - Adrian Wajer
- Dental Primus, Poznańska 18, 88-100 Inowrocław, Poland
| | - Veronica Kiian
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Anna Kloska
- The Faculty of Medicine, Bydgoszcz University of Science and Technology, Kaliskiego 7, 85-796 Bydgoszcz, Poland
| | - Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Joanna Janiszewska-Olszowska
- Department of Interdisciplinary Dentistry, Pomeranian Medical University in Szczecin, Al. Powstańców Wlkp. 72, 70-111 Szczecin, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, University Hospital no 1 in Bydgoszcz, Marii Skłodowskiej Curie 9, 85-094 Bydgoszcz, Poland
| |
Collapse
|
4
|
Kazimierczak N, Kazimierczak W, Serafin Z, Nowicki P, Nożewski J, Janiszewska-Olszowska J. AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review. J Clin Med 2024; 13:344. [PMID: 38256478 PMCID: PMC10816993 DOI: 10.3390/jcm13020344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 01/24/2024] Open
Abstract
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.
Collapse
Affiliation(s)
- Natalia Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Wojciech Kazimierczak
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Zbigniew Serafin
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Paweł Nowicki
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland
| | - Jakub Nożewski
- Department of Emeregncy Medicine, University Hospital No 2 in Bydgoszcz, Ujejskiego 75, 85-168 Bydgoszcz, Poland
| | | |
Collapse
|
5
|
Hadzic A, Urschler M, Press JNA, Riedl R, Rugani P, Štern D, Kirnbauer B. Evaluating a Periapical Lesion Detection CNN on a Clinically Representative CBCT Dataset-A Validation Study. J Clin Med 2023; 13:197. [PMID: 38202204 PMCID: PMC10779652 DOI: 10.3390/jcm13010197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/20/2023] [Accepted: 12/25/2023] [Indexed: 01/12/2024] Open
Abstract
The aim of this validation study was to comprehensively evaluate the performance and generalization capability of a deep learning-based periapical lesion detection algorithm on a clinically representative cone-beam computed tomography (CBCT) dataset and test for non-inferiority. The evaluation involved 195 CBCT images of adult upper and lower jaws, where sensitivity and specificity metrics were calculated for all teeth, stratified by jaw, and stratified by tooth type. Furthermore, each lesion was assigned a periapical index score based on its size to enable a score-based evaluation. Non-inferiority tests were conducted with proportions of 90% for sensitivity and 82% for specificity. The algorithm achieved an overall sensitivity of 86.7% and a specificity of 84.3%. The non-inferiority test indicated the rejection of the null hypothesis for specificity but not for sensitivity. However, when excluding lesions with a periapical index score of one (i.e., very small lesions), the sensitivity improved to 90.4%. Despite the challenges posed by the dataset, the algorithm demonstrated promising results. Nevertheless, further improvements are needed to enhance the algorithm's robustness, particularly in detecting very small lesions and the handling of artifacts and outliers commonly encountered in real-world clinical scenarios.
Collapse
Affiliation(s)
- Arnela Hadzic
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria; (A.H.); (R.R.)
| | - Martin Urschler
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria; (A.H.); (R.R.)
| | - Jan-Niclas Aaron Press
- Division of Oral Surgery and Orthodontics, Medical University of Graz, 8010 Graz, Austria (P.R.); (B.K.)
| | - Regina Riedl
- Institute for Medical Informatics, Statistics and Documentation, Medical University of Graz, 8036 Graz, Austria; (A.H.); (R.R.)
| | - Petra Rugani
- Division of Oral Surgery and Orthodontics, Medical University of Graz, 8010 Graz, Austria (P.R.); (B.K.)
| | - Darko Štern
- Institute of Computer Graphics and Vision, Graz University of Technology, 8010 Graz, Austria
| | - Barbara Kirnbauer
- Division of Oral Surgery and Orthodontics, Medical University of Graz, 8010 Graz, Austria (P.R.); (B.K.)
| |
Collapse
|
6
|
Ahmed ZH, Almuharib AM, Abdulkarim AA, Alhassoon AH, Alanazi AF, Alhaqbani MA, Alshalawi MS, Almuqayrin AK, Almahmoud MI. Artificial Intelligence and Its Application in Endodontics: A Review. J Contemp Dent Pract 2023; 24:912-917. [PMID: 38238281 DOI: 10.5005/jp-journals-10024-3593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
AIM AND BACKGROUND Artificial intelligence (AI) since it was introduced into dentistry, has become an important and valuable tool in many fields. It was applied in different specialties with different uses, for example, in diagnosis of oral cancer, periodontal disease and dental caries, and in the treatment planning and predicting the outcome of orthognathic surgeries. The aim of this comprehensive review is to report on the application and performance of AI models designed for application in the field of endodontics. MATERIALS AND METHODS PubMed, Web of Science, and Google Scholar were searched to collect the most relevant articles using terms, such as AI, endodontics, and dentistry. This review included 56 papers related to AI and its application in endodontics. RESULT The applications of AI were in detecting and diagnosing periapical lesions, assessing root fractures, working length determination, prediction for postoperative pain, studying root canal anatomy and decision-making in endodontics for retreatment. The accuracy of AI in performing these tasks can reach up to 90%. CONCLUSION Artificial intelligence has valuable applications in the field of modern endodontics with promising results. Larger and multicenter data sets can give external validity to the AI models. CLINICAL SIGNIFICANCE In the field of dentistry, AI models are specifically crafted to contribute to the diagnosis of oral diseases, ranging from common issues such as dental caries to more complex conditions like periodontal diseases and oral cancer. AI models can help in diagnosis, treatment planning, and in patient management in endodontics. Along with the modern tools like cone-beam computed tomography (CBCT), AI can be a valuable aid to the clinician. How to cite this article: Ahmed ZH, Almuharib AM, Abdulkarim AA, et al. Artificial Intelligence and Its Application in Endodontics: A Review. J Contemp Dent Pract 2023;24(11):912-917.
Collapse
Affiliation(s)
- Zeeshan Heera Ahmed
- Department of Restorative Dental Sciences and Endodontics, College of Dentistry, King Saud University, Riyadh, Saudi Arabia, Phone: +966502318766, e-mail:
| | | | | | | | | | | | | | | | | |
Collapse
|