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Szabó V, Szabó BT, Orhan K, Veres DS, Manulis D, Ezhov M, Sanders A. Validation of artificial intelligence application for dental caries diagnosis on intraoral bitewing and periapical radiographs. J Dent 2024; 147:105105. [PMID: 38821394 DOI: 10.1016/j.jdent.2024.105105] [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: 10/10/2023] [Revised: 05/21/2024] [Accepted: 05/28/2024] [Indexed: 06/02/2024] Open
Abstract
OBJECTIVES This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs. METHODS The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independent observers using an AI-based (Diagnocat) system. The presence or absence of carious lesions was recorded during Phase 1. After 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiographic data (Phase 3). Subsequently, data reflecting human disagreements were excluded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of Diagnocat, were calculated. RESULTS During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were κ=0.66-1, κ=0.58-0.7, and κ=0.49-0.7. The Fleiss kappa values were κ=0.57-0.8. The sensitivity, specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively. CONCLUSIONS The Diagnocat CNN supports the evaluation of intraoral radiographs for caries diagnosis, as determined by consensus between human and AI system observers. CLINICAL SIGNIFICANCE Our study may aid in the understanding of deep learning-based systems developed for dental imaging modalities for dentists and contribute to expanding the body of results in the field of AI-supported dental radiology..
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Affiliation(s)
- Viktor Szabó
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary
| | - Bence Tamás Szabó
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary.
| | - Kaan Orhan
- Department of Oral Diagnostics, Faculty of Dentistry, Semmelweis University, Budapest, Hungary; Department of Dentomaxillofacial Radiology, Faculty of Dentistry, Ankara University, Ankara, Turkey; Medical Design Application, and Research Center (MEDITAM), Ankara University, Ankara, Turkey
| | - Dániel Sándor Veres
- Department of Biophysics and Radiation Biology, Semmelweis University, Budapest, Hungary
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Yeslam HE, Freifrau von Maltzahn N, Nassar HM. Revolutionizing CAD/CAM-based restorative dental processes and materials with artificial intelligence: a concise narrative review. PeerJ 2024; 12:e17793. [PMID: 39040936 PMCID: PMC11262301 DOI: 10.7717/peerj.17793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Accepted: 07/01/2024] [Indexed: 07/24/2024] Open
Abstract
Artificial intelligence (AI) is increasingly prevalent in biomedical and industrial development, capturing the interest of dental professionals and patients. Its potential to improve the accuracy and speed of dental procedures is set to revolutionize dental care. The use of AI in computer-aided design/computer-aided manufacturing (CAD/CAM) within the restorative dental and material science fields offers numerous benefits, providing a new dimension to these practices. This study aims to provide a concise overview of the implementation of AI-powered technologies in CAD/CAM restorative dental procedures and materials. A comprehensive literature search was conducted using keywords from 2000 to 2023 to obtain pertinent information. This method was implemented to guarantee a thorough investigation of the subject matter. Keywords included; "Artificial Intelligence", "Machine Learning", "Neural Networks", "Virtual Reality", "Digital Dentistry", "CAD/CAM", and "Restorative Dentistry". Artificial intelligence in digital restorative dentistry has proven to be highly beneficial in various dental CAD/CAM applications. It helps in automating and incorporating esthetic factors, occlusal schemes, and previous practitioners' CAD choices in fabricating dental restorations. AI can also predict the debonding risk of CAD/CAM restorations and the compositional effects on the mechanical properties of its materials. Continuous enhancements are being made to overcome its limitations and open new possibilities for future developments in this field.
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Affiliation(s)
- Hanin E. Yeslam
- Department of Restorative Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
| | | | - Hani M. Nassar
- Department of Restorative Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia
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Sim SY, Hwang J, Ryu J, Kim H, Kim EJ, Lee JY. Differential Diagnosis of OKC and SBC on Panoramic Radiographs: Leveraging Deep Learning Algorithms. Diagnostics (Basel) 2024; 14:1144. [PMID: 38893670 PMCID: PMC11172000 DOI: 10.3390/diagnostics14111144] [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: 04/08/2024] [Revised: 05/28/2024] [Accepted: 05/28/2024] [Indexed: 06/21/2024] Open
Abstract
This study aims to determine whether it can distinguish odontogenic keratocyst (OKC) and simple bone cyst (SBC) based solely on preoperative panoramic radiographs through a deep learning algorithm. (1) Methods: We conducted a retrospective analysis of patient data from January 2018 to December 2022 at Pusan National University Dental Hospital. This study included 63 cases of OKC confirmed by histological examination after surgical excision and 125 cases of SBC that underwent surgical curettage. All panoramic radiographs were obtained utilizing the Proline XC system (Planmeca Co., Helsinki, Finland), which already had diagnostic data on them. The panoramic images were cut into 299 × 299 cropped sizes and divided into 80% training and 20% validation data sets for 5-fold cross-validation. Inception-ResNet-V2 system was adopted to train for OKC and SBC discrimination. (2) Results: The classification network for diagnostic performance evaluation achieved 0.829 accuracy, 0.800 precision, 0.615 recall, and a 0.695 F1 score. (4) Conclusions: The deep learning algorithm demonstrated notable accuracy in distinguishing OKC from SBC, facilitated by CAM visualization. This progress is expected to become an essential resource for clinicians, improving diagnostic and treatment outcomes.
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Affiliation(s)
- Su-Yi Sim
- Department of Oral and Maxillofacial Surgery, Dental and Life Science Institute & Dental Research Institute, School of Dentistry, Pusan National University, Yangsan 50612, Republic of Korea; (S.-Y.S.); (J.R.); (H.K.)
| | - JaeJoon Hwang
- Department of Oral and Maxillofacial Radiology, Dental and Life Science Institute & Dental Research Institute, School of Dentistry, Pusan National University, Yangsan 50612, Republic of Korea;
| | - Jihye Ryu
- Department of Oral and Maxillofacial Surgery, Dental and Life Science Institute & Dental Research Institute, School of Dentistry, Pusan National University, Yangsan 50612, Republic of Korea; (S.-Y.S.); (J.R.); (H.K.)
| | - Hyeonjin Kim
- Department of Oral and Maxillofacial Surgery, Dental and Life Science Institute & Dental Research Institute, School of Dentistry, Pusan National University, Yangsan 50612, Republic of Korea; (S.-Y.S.); (J.R.); (H.K.)
| | - Eun-Jung Kim
- Department of Dental Anesthesia and Pain Medicine, School of Dentistry, Pusan National University, Yangsan 50612, Republic of Korea;
| | - Jae-Yeol Lee
- Department of Oral and Maxillofacial Surgery, Dental and Life Science Institute & Dental Research Institute, School of Dentistry, Pusan National University, Yangsan 50612, Republic of Korea; (S.-Y.S.); (J.R.); (H.K.)
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Mărginean AC, Mureşanu S, Hedeşiu M, Dioşan L. Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks' ensemble. Heliyon 2024; 10:e30836. [PMID: 38803980 PMCID: PMC11128823 DOI: 10.1016/j.heliyon.2024.e30836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 03/27/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024] Open
Abstract
Background Dental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy. Method The first model of CariSeg, trained using the U-Net architecture, segments the area of interest, the teeth, and crops the radiograph around it. The next component segments the carious lesions and it is an ensemble composed of three architectures: U-Net, Feature Pyramid Network, and DeeplabV3. For tooth identification two merged datasets were used: The Tufts Dental Database consisting of 1000 panoramic radiography images and another dataset of 116 anonymized panoramic X-rays, taken at Noor Medical Imaging Center, Qom. For carious lesion segmentation, a dataset consisting of 150 panoramic X-ray images was acquired from the Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca. Results The experiments demonstrate that our approach results in 99.42% accuracy and a mean 68.2% Dice coefficient. Conclusions AI helps in detecting carious lesions by analyzing dental X-rays and identifying cavities that might be missed by human observers, leading to earlier detection and treatment of cavities and resulting in better oral health outcomes.
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Affiliation(s)
- Andra Carmen Mărginean
- Computer Science Department, Babes Bolyai University, Mihail Kogalniceanu 1, Cluj-Napoca, 400347, Cluj, Romania
| | - Sorana Mureşanu
- Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Haţieganu University of Medicine and Pharmacy, Victor Babes, 8, Cluj-Napoca, 400012, Cluj, Romania
| | - Mihaela Hedeşiu
- Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Haţieganu University of Medicine and Pharmacy, Victor Babes, 8, Cluj-Napoca, 400012, Cluj, Romania
| | - Laura Dioşan
- Computer Science Department, Babes Bolyai University, Mihail Kogalniceanu 1, Cluj-Napoca, 400347, Cluj, Romania
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Lee T, Shin W, Lee JH, Lee S, Yeom HG, Yun JP. Resolving the non-uniformity in the feature space of age estimation: A deep learning model based on feature clusters of panoramic images. Comput Med Imaging Graph 2024; 112:102329. [PMID: 38271869 DOI: 10.1016/j.compmedimag.2024.102329] [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: 07/13/2023] [Revised: 11/04/2023] [Accepted: 12/13/2023] [Indexed: 01/27/2024]
Abstract
Age estimation is important in forensics, and numerous techniques have been investigated to estimate age based on various parts of the body. Among them, dental tissue is considered reliable for estimating age as it is less influenced by external factors. The advancement in deep learning has led to the development of automatic estimation of age using dental panoramic images. Typically, most of the medical datasets used for model learning are non-uniform in the feature space. This causes the model to be highly influenced by dense feature areas, resulting in adequate estimations; however, relatively poor estimations are observed in other areas. An effective solution to address this issue can be pre-dividing the data by age feature and training each regressor to estimate the age for individual features. In this study, we divide the data based on feature clusters obtained from unsupervised learning. The developed model comprises a classification head and multi-regression head, wherein the former predicts the cluster to which the data belong and the latter estimates the age within the predicted cluster. The visualization results show that the model can focus on a clinically meaningful area in each cluster for estimating age. The proposed model outperforms the models without feature clusters by focusing on the differences within the area. The performance improvement is particularly noticeable in the growth and aging periods. Furthermore, the model can adequately estimate the age even for samples with a high probability of classification error as they are located at the border of two feature clusters.
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Affiliation(s)
- Taehan Lee
- AI research center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu 42994, South Korea
| | - WooSang Shin
- AI research center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu 42994, South Korea; Electronic Engineering Department, Kyungpook National University, Daegu 41566, South Korea
| | - Jong-Hyeon Lee
- AI research center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu 42994, South Korea; Electronic Engineering Department, Kyungpook National University, Daegu 41566, South Korea
| | - Sangmoon Lee
- Electronic Engineering Department, Kyungpook National University, Daegu 41566, South Korea
| | - Han-Gyeol Yeom
- Department of Oral and Maxillofacial Radiology and Wonkwang Dental Research Institute, College of Dentistry, Wonkwang University, Iksan 54538, South Korea.
| | - Jong Pil Yun
- AI research center for Manufacturing Systems (AIMS), Korea Institute of Industrial Technology (KITECH), Daegu 42994, South Korea; University of Science and Technology, Daegu 42994, South Korea.
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Zanini LGK, Rubira-Bullen IRF, Nunes FDLDS. A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01054-5. [PMID: 38429559 DOI: 10.1007/s10278-024-01054-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 12/19/2023] [Accepted: 01/02/2024] [Indexed: 03/03/2024]
Abstract
Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing. Therefore, the survey and systematization of these methods are essential to determining the main computational approaches for identifying caries in X-ray images. In this systematic review, we investigated the primary computational methods used for classifying, detecting, and segmenting caries in X-ray images. Following the PRISMA methodology, we selected relevant studies and analyzed their methods, strengths, limitations, imaging modalities, evaluation metrics, datasets, and classification techniques. The review encompassed 42 studies retrieved from the Science Direct, IEEExplore, ACM Digital, and PubMed databases from the Computer Science and Health areas. The results indicate that 12% of the included articles utilized public datasets, with deep learning being the predominant approach, accounting for 69% of the studies. The majority of these studies (76%) focused on classifying dental caries, either in binary or multiclass classification. Panoramic imaging was the most commonly used radiographic modality, representing 29% of the cases studied. Overall, our systematic review provides a comprehensive overview of the computational methods employed in identifying caries in radiographic images and highlights trends, patterns, and challenges in this research field.
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Affiliation(s)
- Luiz Guilherme Kasputis Zanini
- Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, São Paulo, 05508-010, São Paulo, Brazil.
| | | | - Fátima de Lourdes Dos Santos Nunes
- Department of Computer Engineering and Digital Systems, University of São Paulo, Av. Prof. Luciano Gualberto 158, São Paulo, 05508-010, São Paulo, Brazil
- School of Arts, Sciences and Humanities, University of São Paulo, Rua Arlindo Béttio, 1000, São Paulo, 03828-000, São Paulo, Brazil
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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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Park EY, Jeong S, Kang S, Cho J, Cho JY, Kim EK. Tooth caries classification with quantitative light-induced fluorescence (QLF) images using convolutional neural network for permanent teeth in vivo. BMC Oral Health 2023; 23:981. [PMID: 38066624 PMCID: PMC10709920 DOI: 10.1186/s12903-023-03669-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 11/14/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND Owing to the remarkable advancements of artificial intelligence (AI) applications, AI-based detection of dental caries is continuously improving. We evaluated the efficacy of the detection of dental caries with quantitative light-induced fluorescence (QLF) images using a convolutional neural network (CNN) model. METHODS Overall, 2814 QLF intraoral images were obtained from 606 participants at a dental clinic using Qraypen C® (QC, AIOBIO, Seoul, Republic of Korea) from October 2020 to October 2022. These images included all the types of permanent teeth of which surfaces were smooth or occlusal. Dataset were randomly assigned to the training (56.0%), validation (14.0%), and test (30.0%) subsets of the dataset for caries classification. Moreover, masked images for teeth area were manually prepared to evaluate the segmentation efficacy. To compare diagnostic performance for caries classification according to the types of teeth, the dataset was further classified into the premolar (1,143 images) and molar (1,441 images) groups. As the CNN model, Xception was applied. RESULTS Using the original QLF images, the performance of the classification algorithm was relatively good showing 83.2% of accuracy, 85.6% of precision, and 86.9% of sensitivity. After applying the segmentation process for the tooth area, all the performance indics including 85.6% of accuracy, 88.9% of precision, and 86.9% of sensitivity were improved. However, the performance indices of each type of teeth (both premolar and molar) were similar to those for all teeth. CONCLUSION The application of AI to QLF images for caries classification demonstrated a good performance regardless of teeth type among posterior teeth. Additionally, tooth area segmentation through background elimination from QLF images exhibited a better performance.
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Affiliation(s)
- Eun Young Park
- Department of Dentistry, College of Medicine, Yeungnam University, Daegu, South Korea
| | - Sungmoon Jeong
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea
- Department of Medical Informatics, School of Medicine, Kyungpook National University, Daegu, South Korea
| | - Sohee Kang
- Department of Dentistry, College of Medicine, Yeungnam University, Daegu, South Korea
| | - Jungrae Cho
- Research Center for Artificial Intelligence in Medicine, Kyungpook National University Hospital, Daegu, South Korea
| | - Ju-Yeon Cho
- Department of Dentistry, Dongsan Hospital, Keimyung University School of Medicine, Daegu, South Korea
| | - Eun-Kyong Kim
- Department of Dental Hygiene, College of Science and Technology, Kyungpook National University, Sangju, South Korea.
- , 2559 Gyeongsangde-ro, Sangju, Gyeongsangbuk-do, South Korea.
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Katar O, Yildirim O. An Explainable Vision Transformer Model Based White Blood Cells Classification and Localization. Diagnostics (Basel) 2023; 13:2459. [PMID: 37510202 PMCID: PMC10378025 DOI: 10.3390/diagnostics13142459] [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: 07/11/2023] [Accepted: 07/21/2023] [Indexed: 07/30/2023] Open
Abstract
White blood cells (WBCs) are crucial components of the immune system that play a vital role in defending the body against infections and diseases. The identification of WBCs subtypes is useful in the detection of various diseases, such as infections, leukemia, and other hematological malignancies. The manual screening of blood films is time-consuming and subjective, leading to inconsistencies and errors. Convolutional neural networks (CNN)-based models can automate such classification processes, but are incapable of capturing long-range dependencies and global context. This paper proposes an explainable Vision Transformer (ViT) model for automatic WBCs detection from blood films. The proposed model uses a self-attention mechanism to extract features from input images. Our proposed model was trained and validated on a public dataset of 16,633 samples containing five different types of WBCs. As a result of experiments on the classification of five different types of WBCs, our model achieved an accuracy of 99.40%. Moreover, the model's examination of misclassified test samples revealed a correlation between incorrect predictions and the presence or absence of granules in the cell samples. To validate this observation, we divided the dataset into two classes, Granulocytes and Agranulocytes, and conducted a secondary training process. The resulting ViT model, trained for binary classification, achieved impressive performance metrics during the test phase, including an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43%. To ensure the reliability of the ViT model's, we employed the Score-CAM algorithm to visualize the pixel areas on which the model focuses during its predictions. Our proposed method is suitable for clinical use due to its explainable structure as well as its superior performance compared to similar studies in the literature. The classification and localization of WBCs with this model can facilitate the detection and reporting process for the pathologist.
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Affiliation(s)
- Oguzhan Katar
- Department of Software Engineering, Firat University, Elazig 23119, Turkey
| | - Ozal Yildirim
- Department of Software Engineering, Firat University, Elazig 23119, Turkey
- Department of Artificial Intelligence and Data Engineering, Firat University, Elazig 23119, Turkey
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Anil S, Porwal P, Porwal A. Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques. Cureus 2023; 15:e41694. [PMID: 37575741 PMCID: PMC10413921 DOI: 10.7759/cureus.41694] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/15/2023] Open
Abstract
Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.
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Affiliation(s)
| | - Priyanka Porwal
- Dentistry, Pushpagiri Institute of Medical Sciences and Research Centre, Tiruvalla, IND
| | - Amit Porwal
- Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan, SAU
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