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Sillmann YM, Monteiro JLGC, Eber P, Baggio AMP, Peacock ZS, Guastaldi FPS. Empowering surgeons: will artificial intelligence change oral and maxillofacial surgery? Int J Oral Maxillofac Surg 2024:S0901-5027(24)00369-2. [PMID: 39341693 DOI: 10.1016/j.ijom.2024.09.004] [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: 07/01/2024] [Revised: 09/03/2024] [Accepted: 09/10/2024] [Indexed: 10/01/2024]
Abstract
Artificial Intelligence (AI) can enhance the precision and efficiency of diagnostics and treatments in oral and maxillofacial surgery (OMS), leveraging advanced computational technologies to mimic intelligent human behaviors. The study aimed to examine the current state of AI in the OMS literature and highlight the urgent need for further research to optimize AI integration in clinical practice and enhance patient outcomes. A scoping review of journals related to OMS focused on OMS-related applications. PubMed was searched using terms "artificial intelligence", "convolutional networks", "neural networks", "machine learning", "deep learning", and "automation". Ninety articles were analyzed and classified into the following subcategories: pathology, orthognathic surgery, facial trauma, temporomandibular joint disorders, dentoalveolar surgery, dental implants, craniofacial deformities, reconstructive surgery, aesthetic surgery, and complications. There was a significant increase in AI-related studies published after 2019, 95.6% of the total reviewed. This surge in research reflects growing interest in AI and its potential in OMS. Among the studies, the primary uses of AI in OMS were in pathology (e.g., lesion detection, lymph node metastasis detection) and orthognathic surgery (e.g., surgical planning through facial bone segmentation). The studies predominantly employed convolutional neural networks (CNNs) and artificial neural networks (ANNs) for classification tasks, potentially improving clinical outcomes.
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Affiliation(s)
- Y M Sillmann
- Division of Oral and Maxillofacial Surgery, Massachusetts General Hospital, and Department of Oral and Maxillofacial Surgery, Harvard School of Dental Medicine, Boston, MA, USA
| | - J L G C Monteiro
- Wellman Center for Photomedicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - P Eber
- Division of Oral and Maxillofacial Surgery, Massachusetts General Hospital, and Department of Oral and Maxillofacial Surgery, Harvard School of Dental Medicine, Boston, MA, USA
| | - A M P Baggio
- Division of Oral and Maxillofacial Surgery, Massachusetts General Hospital, and Department of Oral and Maxillofacial Surgery, Harvard School of Dental Medicine, Boston, MA, USA
| | - Z S Peacock
- Division of Oral and Maxillofacial Surgery, Massachusetts General Hospital, and Department of Oral and Maxillofacial Surgery, Harvard School of Dental Medicine, Boston, MA, USA
| | - F P S Guastaldi
- Division of Oral and Maxillofacial Surgery, Massachusetts General Hospital, and Department of Oral and Maxillofacial Surgery, Harvard School of Dental Medicine, Boston, MA, USA.
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Wu Z, Yu X, Chen Y, Chen X, Xu C. Deep learning in the diagnosis of maxillary sinus diseases: a systematic review. Dentomaxillofac Radiol 2024; 53:354-362. [PMID: 38995816 PMCID: PMC11358632 DOI: 10.1093/dmfr/twae031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2024] [Revised: 06/21/2024] [Accepted: 06/30/2024] [Indexed: 07/14/2024] Open
Abstract
OBJECTIVES To assess the performance of deep learning (DL) in the detection, classification, and segmentation of maxillary sinus diseases. METHODS An electronic search was conducted by two reviewers on databases including PubMed, Scopus, Cochrane, and IEEE. All English papers published no later than February 7, 2024, were evaluated. Studies related to DL for diagnosing maxillary sinus diseases were also searched in journals manually. RESULTS Fourteen of 1167 studies were eligible according to the inclusion criteria. All studies trained DL models based on radiographic images. Six studies applied to detection tasks, one focused on classification, two segmented lesions, and five studies made a combination of two types of DL models. The accuracy of the DL algorithms ranged from 75.7% to 99.7%, and the area under curves (AUC) varied between 0.7 and 0.997. CONCLUSION DL can accurately deal with the tasks of diagnosing maxillary sinus diseases. Students, residents, and dentists could be assisted by DL algorithms to diagnose and make rational decisions on implant treatment related to maxillary sinuses.
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Affiliation(s)
- Ziang Wu
- Department of Prosthodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, 200125, China
- National Center for Stomatology, Shanghai, 200011, China
- National Clinical Research Center for Oral Diseases, Shanghai, 200011, China
- Shanghai Key Laboratory of Stomatology, Shanghai, 200011, China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, China
| | - Xinbo Yu
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, 200125, China
- National Center for Stomatology, Shanghai, 200011, China
- National Clinical Research Center for Oral Diseases, Shanghai, 200011, China
- Shanghai Key Laboratory of Stomatology, Shanghai, 200011, China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, China
- Second Dental Center, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201999, China
| | - Yizhou Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Xiaojun Chen
- Institute of Biomedical Manufacturing and Life Quality Engineering, State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Chun Xu
- Department of Prosthodontics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, 200125, China
- National Center for Stomatology, Shanghai, 200011, China
- National Clinical Research Center for Oral Diseases, Shanghai, 200011, China
- Shanghai Key Laboratory of Stomatology, Shanghai, 200011, China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, China
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Elgarba BM, Fontenele RC, Mangano F, Jacobs R. Novel AI-based automated virtual implant placement: Artificial versus human intelligence. J Dent 2024; 147:105146. [PMID: 38914182 DOI: 10.1016/j.jdent.2024.105146] [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] [Received: 04/27/2024] [Revised: 06/10/2024] [Accepted: 06/17/2024] [Indexed: 06/26/2024] Open
Abstract
OBJECTIVES To assess quality, clinical acceptance, time-efficiency, and consistency of a novel artificial intelligence (AI)-driven tool for automated presurgical implant planning for single tooth replacement, compared to a human intelligence (HI)-based approach. MATERIALS AND METHODS To validate a novel AI-driven implant placement tool, a dataset of 10 time-matching cone beam computed tomography (CBCT) scans and intra-oral scans (IOS) previously acquired for single mandibular molar/premolar implant placement was included. An AI pre-trained model for implant planning was compared to human expert-based planning, followed by the export, evaluation and comparison of two generic implants-AI-generated and human-generated-for each case. The quality of both approaches was assessed by 12 calibrated dentists through blinded observations using a visual analogue scale (VAS), while clinical acceptance was evaluated through an AI versus HI battle (Turing test). Subsequently, time efficiency and consistency were evaluated and compared between both planning methods. RESULTS Overall, 360 observations were gathered, with 240 dedicated to VAS, of which 95 % (AI) and 96 % (HI) required no major, clinically relevant corrections. In the AI versus HI Turing test (120 observations), 4 cases had matching judgments for AI and HI, with AI favoured in 3 and HI in 3. Additionally, AI completed planning more than twice as fast as HI, taking only 198 ± 33 s compared to 435 ± 92 s (p < 0.05). Furthermore, AI demonstrated higher consistency with zero-degree median surface deviation (MSD) compared to HI (MSD=0.3 ± 0.17 mm). CONCLUSION AI demonstrated expert-quality and clinically acceptable single-implant planning, proving to be more time-efficient and consistent than the HI-based approach. CLINICAL SIGNIFICANCE Presurgical implant planning often requires multidisciplinary collaboration between highly experienced specialists, which can be complex, cumbersome and time-consuming. However, AI-driven implant planning has the potential to allow clinically acceptable planning, significantly more time-efficient and consistent than the human expert.
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Affiliation(s)
- Bahaaeldeen M Elgarba
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, Leuven 3000, Belgium; Department of Prosthodontics, Faculty of Dentistry, Tanta University, Tanta 31511, Egypt
| | - Rocharles Cavalcante Fontenele
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, Leuven 3000, Belgium
| | - Francesco Mangano
- Honorary Professor in Restorative Dental Sciences, Faculty of Dentistry, The University of Hong Kong, Hong Kong, China
| | - Reinhilde Jacobs
- OMFS-IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, Leuven 3000, Belgium; Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden.
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Macrì M, D’Albis V, D’Albis G, Forte M, Capodiferro S, Favia G, Alrashadah AO, García VDF, Festa F. The Role and Applications of Artificial Intelligence in Dental Implant Planning: A Systematic Review. Bioengineering (Basel) 2024; 11:778. [PMID: 39199736 PMCID: PMC11351972 DOI: 10.3390/bioengineering11080778] [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: 06/29/2024] [Revised: 07/24/2024] [Accepted: 07/26/2024] [Indexed: 09/01/2024] Open
Abstract
Artificial intelligence (AI) is revolutionizing dentistry, offering new opportunities to improve the precision and efficiency of implantology. This literature review aims to evaluate the current evidence on the use of AI in implant planning assessment. The analysis was conducted through PubMed and Scopus search engines, using a combination of relevant keywords, including "artificial intelligence implantology", "AI implant planning", "AI dental implant", and "implantology artificial intelligence". Selected articles were carefully reviewed to identify studies reporting data on the effectiveness of AI in implant planning. The results of the literature review indicate a growing interest in the application of AI in implant planning, with evidence suggesting an improvement in precision and predictability compared to traditional methods. The summary of the obtained findings by the included studies represents the latest AI developments in implant planning, demonstrating its application for the automated detection of bones, the maxillary sinus, neuronal structure, and teeth. However, some disadvantages were also identified, including the need for high-quality training data and the lack of standardization in protocols. In conclusion, the use of AI in implant planning presents promising prospects for improving clinical outcomes and optimizing patient management. However, further research is needed to fully understand its potential and address the challenges associated with its implementation in clinical practice.
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Affiliation(s)
- Monica Macrì
- Department of Innovative Technologies in Medicine & Dentistry, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy; (V.D.); (F.F.)
| | - Vincenzo D’Albis
- Department of Innovative Technologies in Medicine & Dentistry, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy; (V.D.); (F.F.)
| | - Giuseppe D’Albis
- Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy; (G.D.); (M.F.); (S.C.); (G.F.)
| | - Marta Forte
- Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy; (G.D.); (M.F.); (S.C.); (G.F.)
| | - Saverio Capodiferro
- Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy; (G.D.); (M.F.); (S.C.); (G.F.)
| | - Gianfranco Favia
- Department of Interdisciplinary Medicine, University of Bari Aldo Moro, 70121 Bari, Italy; (G.D.); (M.F.); (S.C.); (G.F.)
| | | | - Victor Diaz-Flores García
- Department of Pre-Clinical Dentistry, School of Biomedical Sciences, Universidad Europea de Madrid, Villaviciosa de Odón, 28670 Madrid, Spain;
| | - Felice Festa
- Department of Innovative Technologies in Medicine & Dentistry, University “G. D’Annunzio” of Chieti-Pescara, 66100 Chieti, Italy; (V.D.); (F.F.)
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Kazimierczak W, Kazimierczak N, Kędziora K, Szcześniak M, Serafin Z. Reliability of the AI-Assisted Assessment of the Proximity of the Root Apices to Mandibular Canal. J Clin Med 2024; 13:3605. [PMID: 38930132 PMCID: PMC11204399 DOI: 10.3390/jcm13123605] [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: 06/06/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 06/28/2024] Open
Abstract
Background: This study evaluates the diagnostic accuracy of an AI-assisted tool in assessing the proximity of the mandibular canal (MC) to the root apices (RAs) of mandibular teeth using computed tomography (CT). Methods: This study involved 57 patients aged 18-30 whose CT scans were analyzed by both AI and human experts. The primary aim was to measure the closest distance between the MC and RAs and to assess the AI tool's diagnostic performance. The results indicated significant variability in RA-MC distances, with third molars showing the smallest mean distances and first molars the greatest. Diagnostic accuracy metrics for the AI tool were assessed at three thresholds (0 mm, 0.5 mm, and 1 mm). Results: The AI demonstrated high specificity but generally low diagnostic accuracy, with the highest metrics at the 0.5 mm threshold with 40.91% sensitivity and 97.06% specificity. Conclusions: This study underscores the limited potential of tested AI programs in reducing iatrogenic damage to the inferior alveolar nerve (IAN) during dental procedures. Significant differences in RA-MC distances between evaluated teeth were found.
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Affiliation(s)
- Wojciech Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland; (K.K.); (Z.S.)
| | - Natalia Kazimierczak
- Department of Radiology and Diagnostic Imaging, Collegium Medicum, Nicolaus Copernicus University in Torun, Jagiellońska 13-15, 85-067 Bydgoszcz, Poland
| | - Kamila Kędziora
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland; (K.K.); (Z.S.)
| | - Marta Szcześniak
- Chair of Practical Clinical Dentistry, Department of Diagnostics, Poznań University of Medical Sciences, Fredry 10, 61-701 Poznań, Poland;
| | - Zbigniew Serafin
- Kazimierczak Private Medical Practice, Dworcowa 13/u6a, 85-009 Bydgoszcz, Poland; (K.K.); (Z.S.)
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Wu Z, Yu X, Wang F, Xu C. Application of artificial intelligence in dental implant prognosis: A scoping review. J Dent 2024; 144:104924. [PMID: 38467177 DOI: 10.1016/j.jdent.2024.104924] [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] [Received: 11/05/2023] [Revised: 02/19/2024] [Accepted: 03/03/2024] [Indexed: 03/13/2024] Open
Abstract
OBJECTIVES The purpose of this scoping review was to evaluate the performance of artificial intelligence (AI) in the prognosis of dental implants. DATA Studies that analyzed the performance of AI models in the prediction of implant prognosis based on medical records or radiographic images. Quality assessment was conducted using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Quasi-Experimental Studies. SOURCES This scoping review included studies published in English up to October 2023 in MEDLINE/PubMed, Embase, Cochrane Library, and Scopus. A manual search was also performed. STUDY SELECTION Of 892 studies, full-text analysis was conducted in 36 studies. Twelve studies met the inclusion criteria. Eight used deep learning models, 3 applied traditional machine learning algorithms, and 1 study combined both types. The performance was quantified using accuracy, sensitivity, specificity, precision, F1 score, and receiver operating characteristic area under curves (ROC AUC). The prognostic accuracy was analyzed and ranged from 70 % to 96.13 %. CONCLUSIONS AI is a promising tool in evaluating implant prognosis, but further enhancements are required. Additional radiographic and clinical data are needed to improve AI performance in implant prognosis. CLINICAL SIGNIFICANCE AI can predict the prognosis of dental implants based on radiographic images or medical records. As a result, clinicians can receive predicted implant prognosis with the assistance of AI before implant placement and make informed decisions.
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Affiliation(s)
- Ziang Wu
- Department of Prosthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China
| | - Xinbo Yu
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China; Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Feng Wang
- College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China; Second Dental Center, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Chun Xu
- Department of Prosthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; College of Stomatology, Shanghai Jiao Tong University, Shanghai, China; National Center for Stomatology, Shanghai, China; National Clinical Research Center for Oral Diseases, Shanghai, China; Shanghai Key Laboratory of Stomatology, Shanghai, China; Shanghai Research Institute of Stomatology, Shanghai, China.
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Liu Z, Yang D, Zhang M, Liu G, Zhang Q, Li X. Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions. Bioengineering (Basel) 2024; 11:354. [PMID: 38671776 PMCID: PMC11048269 DOI: 10.3390/bioengineering11040354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 03/29/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024] Open
Abstract
Accurate inferior alveolar nerve (IAN) canal segmentation has been considered a crucial task in dentistry. Failing to accurately identify the position of the IAN canal may lead to nerve injury during dental procedures. While IAN canals can be detected from dental cone beam computed tomography, they are usually difficult for dentists to precisely identify as the canals are thin, small, and span across many slices. This paper focuses on improving accuracy in segmenting the IAN canals. By integrating our proposed frequency-domain attention mechanism in UNet, the proposed frequency attention UNet (FAUNet) is able to achieve 75.55% and 81.35% in the Dice and surface Dice coefficients, respectively, which are much higher than other competitive methods, by adding only 224 parameters to the classical UNet. Compared to the classical UNet, our proposed FAUNet achieves a 2.39% and 2.82% gain in the Dice coefficient and the surface Dice coefficient, respectively. The potential advantage of developing attention in the frequency domain is also discussed, which revealed that the frequency-domain attention mechanisms can achieve better performance than their spatial-domain counterparts.
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Affiliation(s)
- Zhiyang Liu
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
- Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
| | - Dong Yang
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
| | - Minghao Zhang
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
| | - Guohua Liu
- College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
- Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, College of Electronic Information and Optical Engineering, Nankai University, Tianjin 300350, China
| | - Qian Zhang
- School and Hospital of Stomatology, Tianjin Medical University, Tianjin 300070, China
| | - Xiaonan Li
- School and Hospital of Stomatology, Tianjin Medical University, Tianjin 300070, China
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Elgarba BM, Fontenele RC, Tarce M, Jacobs R. Artificial intelligence serving pre-surgical digital implant planning: A scoping review. J Dent 2024; 143:104862. [PMID: 38336018 DOI: 10.1016/j.jdent.2024.104862] [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] [Received: 12/14/2023] [Revised: 01/22/2024] [Accepted: 01/24/2024] [Indexed: 02/12/2024] Open
Abstract
OBJECTIVES To conduct a scoping review focusing on artificial intelligence (AI) applications in presurgical dental implant planning. Additionally, to assess the automation degree of clinically available pre-surgical implant planning software. DATA AND SOURCES A systematic electronic literature search was performed in five databases (PubMed, Embase, Web of Science, Cochrane Library, and Scopus), along with exploring gray literature web-based resources until November 2023. English-language studies on AI-driven tools for digital implant planning were included based on an independent evaluation by two reviewers. An assessment of automation steps in dental implant planning software available on the market up to November 2023 was also performed. STUDY SELECTION AND RESULTS From an initial 1,732 studies, 47 met eligibility criteria. Within this subset, 39 studies focused on AI networks for anatomical landmark-based segmentation, creating virtual patients. Eight studies were dedicated to AI networks for virtual implant placement. Additionally, a total of 12 commonly available implant planning software applications were identified and assessed for their level of automation in pre-surgical digital implant workflows. Notably, only six of these featured at least one fully automated step in the planning software, with none possessing a fully automated implant planning protocol. CONCLUSIONS AI plays a crucial role in achieving accurate, time-efficient, and consistent segmentation of anatomical landmarks, serving the process of virtual patient creation. Additionally, currently available systems for virtual implant placement demonstrate different degrees of automation. It is important to highlight that, as of now, full automation of this process has not been documented nor scientifically validated. CLINICAL SIGNIFICANCE Scientific and clinical validation of AI applications for presurgical dental implant planning is currently scarce. The present review allows the clinician to identify AI-based automation in presurgical dental implant planning and assess the potential underlying scientific validation.
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Affiliation(s)
- Bahaaeldeen M Elgarba
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium & Department of Prosthodontics, Faculty of Dentistry, Tanta University, 31511 Tanta, Egypt.
| | - Rocharles Cavalcante Fontenele
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium
| | - Mihai Tarce
- Division of Periodontology & Implant Dentistry, Faculty of Dentistry, The University of Hong Kong, Hong Kong SAR, China & Periodontology and Oral Microbiology, Department of Oral Health Sciences, Faculty of Medicine, KU Leuven, Leuven, Belgium
| | - Reinhilde Jacobs
- OMFS IMPATH Research Group, Department of Imaging and Pathology, Faculty of Medicine, KU Leuven & Department of Oral and Maxillofacial Surgery, University Hospitals, Campus Sint-Rafael, 3000 Leuven, Belgium & Department of Dental Medicine, Karolinska Institute, Stockholm, Sweden
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