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Albano D, Galiano V, Basile M, Di Luca F, Gitto S, Messina C, Cagetti MG, Del Fabbro M, Tartaglia GM, Sconfienza LM. Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review. BMC Oral Health 2024; 24:274. [PMID: 38402191 PMCID: PMC10894487 DOI: 10.1186/s12903-024-04046-7] [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: 11/11/2023] [Accepted: 02/17/2024] [Indexed: 02/26/2024] Open
Abstract
BACKGROUND The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). MATERIALS AND METHODS An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. RESULTS Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3-0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. CONCLUSION AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. PROTOCOL PROSPERO identifier: CRD42023470708.
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Affiliation(s)
- Domenico Albano
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy.
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy.
| | | | - Mariachiara Basile
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Filippo Di Luca
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Salvatore Gitto
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Carmelo Messina
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
| | - Maria Grazia Cagetti
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Massimo Del Fabbro
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Ospedale Maggiore Policlinico, UOC Maxillo-Facial Surgery and Dentistry Fondazione IRCCS Cà Granda, Milan, Italy
| | - Gianluca Martino Tartaglia
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Ospedale Maggiore Policlinico, UOC Maxillo-Facial Surgery and Dentistry Fondazione IRCCS Cà Granda, Milan, Italy
| | - Luca Maria Sconfienza
- IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
- Department of Biomedical Sciences for Health, University of Milan, Milan, Italy
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ForouzeshFar P, Safaei AA, Ghaderi F, Hashemikamangar SS. Dental Caries diagnosis from bitewing images using convolutional neural networks. BMC Oral Health 2024; 24:211. [PMID: 38341526 PMCID: PMC10858561 DOI: 10.1186/s12903-024-03973-9] [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: 09/10/2023] [Accepted: 02/02/2024] [Indexed: 02/12/2024] Open
Abstract
BACKGROUND Dental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, creating acid that gradually wears away at the tooth structure. Tooth discoloration, pain, and sensitivity to hot or cold foods and drinks are common symptoms of tooth decay. Although this condition is prevalent among all age groups, it is especially prevalent in children with baby teeth. Early diagnosis of dental caries is critical to preventing further decay and avoiding costly tooth repairs. Currently, dentists employ a time-consuming and repetitive process of manually marking tooth lesions after conducting radiographic exams. However, with the rapid development of artificial intelligence in medical imaging research, there is a chance to improve the accuracy and efficiency of dental diagnosis. METHODS This study introduces a data-driven model for accurately diagnosing dental decay through the use of Bitewing radiology images using convolutional neural networks. The dataset utilized in this research includes 713 patient images obtained from the Samin Maxillofacial Radiology Center located in Tehran, Iran. The images were captured between June 2020 and January 2022 and underwent processing via four distinct Convolutional Neural Networks. The images were resized to 100 × 100 and then divided into two groups: 70% (4219) for training and 30% (1813) for testing. The four networks employed in this study were AlexNet, ResNet50, VGG16, and VGG19. RESULTS Among different well-known CNN architectures compared in this study, the VGG19 model was found to be the most accurate, with a 93.93% accuracy. CONCLUSION This promising result indicates the potential for developing an automatic AI-based dental caries diagnostic model from Bitewing images. It has the potential to serve patients or dentists as a mobile app or cloud-based diagnosis service (clinical decision support system).
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Affiliation(s)
- Parsa ForouzeshFar
- Department of Data Science, Faculty of Mathematical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Ali Asghar Safaei
- Department of Medical Informatics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
- Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran.
| | - Foad Ghaderi
- Department of Data Science, Faculty of Interdisciplinary Science and Technology, Tarbiat Modares University, Tehran, Iran
- Human-Computer Interaction Lab, Electrical and Computer Engineering Department, Tarbiat Modares University, Tehran, Iran
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Pathour T, Ma L, Strand DW, Gahan J, Johnson BA, Sirsi SR, Fei B. Feature Extraction of Ultrasound Radiofrequency Data for the Classification of the Peripheral Zone of Human Prostate. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2024; 12932:129321F. [PMID: 38707197 PMCID: PMC11069342 DOI: 10.1117/12.3008643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Prostate cancer ranks among the most prevalent types of cancer in males, prompting a demand for early detection and noninvasive diagnostic techniques. This paper explores the potential of ultrasound radiofrequency (RF) data to study different anatomic zones of the prostate. The study leverages RF data's capacity to capture nuanced acoustic information from clinical transducers. The research focuses on the peripheral zone due to its high susceptibility to cancer. The feasibility of utilizing RF data for classification is evaluated using ex-vivo whole prostate specimens from human patients. Ultrasound data, acquired using a phased array transducer, is processed, and correlated with B-mode images. A range filter is applied to highlight the peripheral zone's distinct features, observed in both RF data and 3D plots. Radiomic features were extracted from RF data to enhance tissue characterization and segmentation. The study demonstrated RF data's ability to differentiate tissue structures and emphasizes its potential for prostate tissue classification, addressing the current limitations of ultrasound imaging for prostate management. These findings advocate for the integration of RF data into ultrasound diagnostics, potentially transforming prostate cancer diagnosis and management in the future.
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Affiliation(s)
- Teja Pathour
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX
- Department of Bioengineering, The University of Texas at Dallas, TX
| | - Ling Ma
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX
- Department of Bioengineering, The University of Texas at Dallas, TX
| | - Douglas W. Strand
- Department of Urology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Jeffrey Gahan
- Department of Urology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Brett A. Johnson
- Department of Urology, The University of Texas Southwestern Medical Center, Dallas, TX
| | - Shashank R. Sirsi
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX
- Department of Bioengineering, The University of Texas at Dallas, TX
| | - Baowei Fei
- Center for Imaging and Surgical Innovation, The University of Texas at Dallas, TX
- Department of Bioengineering, The University of Texas at Dallas, TX
- Department of Radiology, The University of Texas Southwestern Medical Center, Dallas, TX
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Hartoonian S, Hosseini M, Yousefi I, Mahdian M, Ghazizadeh Ahsaie M. Applications of artificial intelligence in dentomaxillofacial imaging-a systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol 2024:S2212-4403(23)01566-3. [PMID: 38637235 DOI: 10.1016/j.oooo.2023.12.790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 12/02/2023] [Accepted: 12/22/2023] [Indexed: 04/20/2024]
Abstract
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.
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Affiliation(s)
- Serlie Hartoonian
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Matine Hosseini
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Iman Yousefi
- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mina Mahdian
- Department of Prosthodontics and Digital Technology, Stony Brook University School of Dental Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Mitra Ghazizadeh Ahsaie
- Department of Oral and Maxillofacial Radiology, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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Tabatabaian F, Vora SR, Mirabbasi S. Applications, functions, and accuracy of artificial intelligence in restorative dentistry: A literature review. J ESTHET RESTOR DENT 2023; 35:842-859. [PMID: 37522291 DOI: 10.1111/jerd.13079] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/18/2023] [Accepted: 06/19/2023] [Indexed: 08/01/2023]
Abstract
OBJECTIVE The applications of artificial intelligence (AI) are increasing in restorative dentistry; however, the AI performance is unclear for dental professionals. The purpose of this narrative review was to evaluate the applications, functions, and accuracy of AI in diverse aspects of restorative dentistry including caries detection, tooth preparation margin detection, tooth restoration design, metal structure casting, dental restoration/implant detection, removable partial denture design, and tooth shade determination. OVERVIEW An electronic search was performed on Medline/PubMed, Embase, Web of Science, Cochrane, Scopus, and Google Scholar databases. English-language articles, published from January 1, 2000, to March 1, 2022, relevant to the aforementioned aspects were selected using the key terms of artificial intelligence, machine learning, deep learning, artificial neural networks, convolutional neural networks, clustering, soft computing, automated planning, computational learning, computer vision, and automated reasoning as inclusion criteria. A manual search was also performed. Therefore, 157 articles were included, reviewed, and discussed. CONCLUSIONS Based on the current literature, the AI models have shown promising performance in the mentioned aspects when being compared with traditional approaches in terms of accuracy; however, as these models are still in development, more studies are required to validate their accuracy and apply them to routine clinical practice. CLINICAL SIGNIFICANCE AI with its specific functions has shown successful applications with acceptable accuracy in diverse aspects of restorative dentistry. The understanding of these functions may lead to novel applications with optimal accuracy for AI in restorative dentistry.
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Affiliation(s)
- Farhad Tabatabaian
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Siddharth R Vora
- Department of Oral Health Sciences, Faculty of Dentistry, The University of British Columbia, Vancouver, British Columbia, Canada
| | - Shahriar Mirabbasi
- Department of Electrical and Computer Engineering, Faculty of Applied Science, The University of British Columbia, Vancouver, British Columbia, Canada
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Zhu J, Chen Z, Zhao J, Yu Y, Li X, Shi K, Zhang F, Yu F, Shi K, Sun Z, Lin N, Zheng Y. Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study. BMC Oral Health 2023; 23:358. [PMID: 37270488 DOI: 10.1186/s12903-023-03027-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 05/09/2023] [Indexed: 06/05/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance. METHODS The AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden's index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05). RESULTS Sensitivity, specificity, and Youden's index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976-0.983, impacted teeth), 0.975 (95%CI: 0.972-0.978, full crowns), and 0.935 (95%CI: 0.929-0.940, residual roots), 0.939 (95%CI: 0.934-0.944, missing teeth), and 0.772 (95%CI: 0.764-0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001). CONCLUSIONS The AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3-10 years of experience. However, the AI framework for caries diagnosis should be improved.
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Affiliation(s)
- Junhua Zhu
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Zhi Chen
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Jing Zhao
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Yueyuan Yu
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Xiaojuan Li
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Kangjian Shi
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Fan Zhang
- College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Feifei Yu
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Keying Shi
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Zhe Sun
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Nengjie Lin
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
| | - Yuanna Zheng
- School/Hospital of Stomatology, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
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Araújo ALD, Moraes MC, Pérez-de-Oliveira ME, Silva VMD, Saldivia-Siracusa C, Pedroso CM, Lopes MA, Vargas PA, Kochanny S, Pearson A, Khurram SA, Kowalski LP, Migliorati CA, Santos-Silva AR. Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis. Oral Oncol 2023; 140:106386. [PMID: 37023561 DOI: 10.1016/j.oraloncology.2023.106386] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 03/20/2023] [Accepted: 03/26/2023] [Indexed: 04/08/2023]
Abstract
INTRODUCTION The aim of the present systematic review (SR) is to summarize Machine Learning (ML) models currently used to predict head and neck cancer (HNC) treatment-related toxicities, and to understand the impact of image biomarkers (IBMs) in prediction models (PMs). The present SR was conducted following the guidelines of the PRISMA 2022 and registered in PROSPERO database (CRD42020219304). METHODS The acronym PICOS was used to develop the focused review question (Can PMs accurately predict HNC treatment toxicities?) and the eligibility criteria. The inclusion criteria enrolled Prediction Model Studies (PMSs) with patient cohorts that were treated for HNC and developed toxicities. Electronic database search encompassed PubMed, EMBASE, Scopus, Cochrane Library, Web of Science, LILACS, and Gray Literature (Google Scholar and ProQuest). Risk of Bias (RoB) was assessed through PROBAST and the results were synthesized based on the data format (with and without IBMs) to allow comparison. RESULTS A total of 28 studies and 4,713 patients were included. Xerostomia was the most frequently investigated toxicity (17; 60.71 %). Sixteen (57.14 %) studies reported using radiomics features in combination with clinical or dosimetrics/dosiomics for modelling. High RoB was identified in 23 studies. Meta-analysis (MA) showed an area under the receiver operating characteristics curve (AUROC) of 0.82 for models with IBMs and 0.81 for models without IBMs (p value < 0.001), demonstrating no difference among IBM- and non-IBM-based models. DISCUSSION The development of a PM based on sample-specific features represents patient selection bias and may affect a model's performance. Heterogeneity of the studies as well as non-standardized metrics prevent proper comparison of studies, and the absence of an independent/external test does not allow the evaluation of the model's generalization ability. CONCLUSION IBM-featured PMs are not superior to PMs based on non-IBM predictors. The evidence was appraised as of low certainty.
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Affiliation(s)
- Anna Luíza Damaceno Araújo
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil; Head and Neck Surgery Department, University of São Paulo Medical School (UFMUSP), São Paulo, São Paulo, Brazil
| | - Matheus Cardoso Moraes
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil
| | | | - Viviane Mariano da Silva
- Institute of Science and Technology, Federal University of São Paulo (ICT-Unifesp), São José dos Campos, São Paulo, Brazil
| | - Cristina Saldivia-Siracusa
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Caique Mariano Pedroso
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Marcio Ajudarte Lopes
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Pablo Agustin Vargas
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil
| | - Sara Kochanny
- Section of Hemathology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, United States; University of Chicago Comprehensive Cancer Center, Chicago, Chicago, IL, United States
| | - Alexander Pearson
- Section of Hemathology/Oncology, Department of Medicine, University of Chicago, Chicago, IL, United States; University of Chicago Comprehensive Cancer Center, Chicago, Chicago, IL, United States
| | - Syed Ali Khurram
- Unit of Oral and Maxillofacial Pathology, School of Clinical Dentistry, University of Sheffield, S10 2TA Sheffield, United Kingdom
| | - Luiz Paulo Kowalski
- Department of Head and Neck Surgery and Otorhinolaryngology, A.C. Camargo Cancer Center, São Paulo, Brazil; Head and Neck Surgery Department and LIM 28, University of São Paulo Medical School, São Paulo, Brazil
| | | | - Alan Roger Santos-Silva
- Oral Diagnosis Department, Piracicaba Dental School, University of Campinas (UNICAMP), Piracicaba, São Paulo, Brazil.
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Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review. Healthcare (Basel) 2022; 10:healthcare10101892. [PMID: 36292339 PMCID: PMC9602147 DOI: 10.3390/healthcare10101892] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 08/30/2022] [Accepted: 08/31/2022] [Indexed: 12/04/2022] Open
Abstract
Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system’s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field.
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Pedroso CM, Migliorati CA, Epstein JB, Ribeiro ACP, Brandão TB, Lopes MA, de Goes MF, Santos-Silva AR. Over 300 Radiation Caries Papers: Reflections From the Rearview Mirror. FRONTIERS IN ORAL HEALTH 2022; 3:961594. [PMID: 35911379 PMCID: PMC9330023 DOI: 10.3389/froh.2022.961594] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
Radiation caries (RC) is an aggressive oral toxicity in head and neck cancer survivors, which develops 6 to 12 months after head and neck radiotherapy. It initially affects the tooth cervical/incisal surfaces, and if not promptly diagnosed/managed, progresses to dental crown amputation and risk of osteoradionecrosis. It results from a multidimensional cluster of treatment-induced oral symptoms, including hyposalivation, dietary changes, and oral hygiene impairment. Although recognized as a frequent complication of radiotherapy and extensively assessed by a myriad of retrospective, in vitro, and in situ studies, RC patients are still orphans of clinically validated methods for risk prediction, prevention, and treatment of early lesions. This review provides a historical overview of science-based concepts regarding RC pathogenesis and treatment, emphasizing the growing demand for interventional clinical studies (randomized trials).
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Affiliation(s)
- Caique Mariano Pedroso
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), Campinas, Brazil
| | | | - Joel B. Epstein
- Cedars-Sinai Medical Center, Samuel Oschin Comprehensive Cancer Institute, Los Angeles, CA, United States
- City of Hope Comprehensive Cancer Center, Duarte, CA, United States
| | | | - Thaís Bianca Brandão
- Cedars-Sinai Medical Center, Samuel Oschin Comprehensive Cancer Institute, Los Angeles, CA, United States
| | - Márcio Ajudarte Lopes
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), Campinas, Brazil
| | - Mário Fernando de Goes
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), Campinas, Brazil
| | - Alan Roger Santos-Silva
- Department of Oral Diagnosis, Piracicaba Dental School, University of Campinas (UNICAMP), Campinas, Brazil
- *Correspondence: Alan Roger Santos-Silva
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Application and Performance of Artificial Intelligence Technology in Detection, Diagnosis and Prediction of Dental Caries (DC)—A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12051083. [PMID: 35626239 PMCID: PMC9139989 DOI: 10.3390/diagnostics12051083] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 04/12/2022] [Accepted: 04/25/2022] [Indexed: 01/27/2023] Open
Abstract
Evolution in the fields of science and technology has led to the development of newer applications based on Artificial Intelligence (AI) technology that have been widely used in medical sciences. AI-technology has been employed in a wide range of applications related to the diagnosis of oral diseases that have demonstrated phenomenal precision and accuracy in their performance. The aim of this systematic review is to report on the diagnostic accuracy and performance of AI-based models designed for detection, diagnosis, and prediction of dental caries (DC). Eminent electronic databases (PubMed, Google scholar, Scopus, Web of science, Embase, Cochrane, Saudi Digital Library) were searched for relevant articles that were published from January 2000 until February 2022. A total of 34 articles that met the selection criteria were critically analyzed based on QUADAS-2 guidelines. The certainty of the evidence of the included studies was assessed using the GRADE approach. AI has been widely applied for prediction of DC, for detection and diagnosis of DC and for classification of DC. These models have demonstrated excellent performance and can be used in clinical practice for enhancing the diagnostic performance, treatment quality and patient outcome and can also be applied to identify patients with a higher risk of developing DC.
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