Dental Diagnosis from X-Ray Images using Fuzzy Rule-Based Systems

Tuấn, Trần Mạnh; Duc, Nguyen Thanh; Hai, Pham Van; Sơn, Lê Hoàng

doi:10.4018/ijfsa.2017010101

Cited by 18 publications

(9 citation statements)

References 24 publications

(21 reference statements)

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“…Periodontal disease, 151 Candidiasis risk factor, 152 other diseases and applications [153][154][155][156][157][158] Clinical/biological data, [151][152][153][154] radiography [155][156][157][158] Biomimetic color analysis and modeling…”

Section: Disease Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Applications of artificial intelligence in dentistry: A comprehensive review

Carrillo-Pérez

Pecho

Morales

et al. 2021

J Esthet Restor Dent

100

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Objective To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions The insight provided by the present work has reported outstanding results in the design of high‐performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

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Section: Disease Identificationmentioning

confidence: 99%

“…Other papers on the use of FL technologies include disease identification using fuzzy rules, 156 the prediction of oral squamous cell carcinoma through fuzzy decision trees, 153 the selection of headgear types through fuzzy rules, 154 and Fuzzy clustering for segmentation tasks on images 154,158 …”

Section: Fuzzy Logicmentioning

confidence: 99%

Applications of artificial intelligence in dentistry: A comprehensive review

Carrillo-Pérez

Pecho

Morales

et al. 2021

J Esthet Restor Dent

100

View full text Add to dashboard Cite

show abstract

“…Các độ đo dùng để đánh giá và so sánh hiệu năng của thuật toán đề xuất với các thuật toán có liên quan bao gồm: Time, Accuracy [14], Recall [14], Precision [14]. Đồng thời, chúng tôi đánh giá hiệu năng của mô hình phát triển GCFSM với một số mô hình đã được nghiên cứu gần đây FIS [15] và SSFCFSM [16]. Kết quả thực nghiệm, đánh giá dựa trên các độ đo đánh giá hiệu năng giữa thuật toán trên cùng bộ dữ liệu.…”

Section: Một Số Kết Quả Thực Nghiệmunclassified

Tính Toán Hạt Dựa Trên Độ Đo Tương Đồng Mờ Phức Trong Hỗ Trợ Chẩn Đoán Nha Khoa

Tuấn¹,

Ngân²,

Tuấn³

2021

TNUJST

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Việc ứng dụng công nghệ trợ giúp trong quá trình thăm khám bệnh là nhân tố quan trọng trong việc giảm tải công việc của bác sĩ. Vấn đề đặt ra là làm thế nào để các công nghệ nâng cao được chính xác trong chẩn đoán. Trong nghiên cứu này, chúng tôi phát triển một mô hình kết hợp giữa tính toán hạt với các độ đo tương đồng mờ phức. Trước hết, độ đo tương đồng mờ phức được sử dụng để đánh giá độ tương đồng giữa các mẫu bệnh chuẩn với mẫu cần chẩn đoán. Sau đó tính toán hạt được áp dụng để lựa chọn xem bệnh nào có khả năng mắc cao nhất. Mô hình đề xuất được thực nghiệm trên bộ dữ liệu ảnh nha khoa bao gồm các ảnh X-quang về bệnh răng khôn mọc lệch. Kết quả thực nghiệm chứng tỏ mô hình mới có độ chính xác tốt hơn so với một số phương pháp khác. Kết quả nghiên cứu này sẽ hỗ trợ các nha sĩ trong chẩn đoán bệnh răng khôn mọc lệch.

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“…Several basic clustering algorithms have been used for medical image segmentation, including Fuzzy clustering for dental radiographs [ 17 ] and K - means clustering to detect breast cancer on digital mammograms [ 18 ]. Clustering is a method of unsupervised learning used to find structures in datasets that are not labeled.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Widyaningrum

Sela

Pulungan

et al. 2023

International Journal of Dentistry

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Osteoporosis leads to the loss of cortical thickness, a decrease in bone mineral density (BMD), deterioration in the size of trabeculae, and an increased risk of fractures. Changes in trabecular bone due to osteoporosis can be observed on periapical radiographs, which are widely used in dental practice. This study proposes an automatic trabecular bone segmentation method for detecting osteoporosis using a color histogram and machine learning (ML), based on 120 regions of interest (ROI) on periapical radiographs, and divided into 60 training and 42 testing datasets. The diagnosis of osteoporosis is based on BMD as evaluated by dual X-ray absorptiometry. The proposed method comprises five stages: the obtaining of ROI images, conversion to grayscale, color histogram segmentation, extraction of pixel distribution, and performance evaluation of the ML classifier. For trabecular bone segmentation, we compare K-means and Fuzzy C-means. The distribution of pixels obtained from the K-means and Fuzzy C-means segmentation was used to detect osteoporosis using three ML methods: decision tree, naive Bayes, and multilayer perceptron. The testing dataset was used to obtain the results in this study. Based on the performance evaluation of the K-means and Fuzzy C-means segmentation methods combined with 3 ML, the osteoporosis detection method with the best diagnostic performance was K-means segmentation combined with a multilayer perceptron classifier, with accuracy, specificity, and sensitivity of 90.48%, 90.90%, and 90.00%, respectively. The high accuracy of this study indicates that the proposed method provides a significant contribution to the detection of osteoporosis in the field of medical and dental image analysis.

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Dental Diagnosis from X-Ray Images using Fuzzy Rule-Based Systems

Cited by 18 publications

References 24 publications

Applications of artificial intelligence in dentistry: A comprehensive review

Applications of artificial intelligence in dentistry: A comprehensive review

Tính Toán Hạt Dựa Trên Độ Đo Tương Đồng Mờ Phức Trong Hỗ Trợ Chẩn Đoán Nha Khoa

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

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