Application of artificial intelligence in the dental field : A literature review

Purpose: The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field, focusing on the evaluation criteria and architecture types. Study selection: Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included. Results: The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on “oral and maxillofacial surgery.” Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve. Conclusion: Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning

Penerapan Fuzzy Logic untuk Menentukan Minuman Susu Kemasan Terbaik dalam Pengoptimalan Gizi

Penelitian terapan ini bertujuan untuk membangun model Penentuan Minuman Susu Kemasan Terbaik dengan variabel pertimbangan adalah harga dan nutrisi. Langkah-langkah yang digunakan pada penelitian ini yaitu fuzzifikasi, penentuan aturan fuzzy, inferensi fuzzy dengan metode mamdani, dan defuzzifikasi. Data yang digunakan adalah data yang diambil dari survey langsung di lapangan yang dilakukan oleh peneliti di salah satu supermarket di makassar. Hasil dari penelitian ini adalah susu kemasan sampel 16 yang menjadi susu kemasan yang paling cocok untuk direkomendasikan kepada masyarakat karena memiliki nutrisi tinggi dan harga yang terjangkau.Kata Kunci: Fuzzy logic, Mamdani, Susu, Harga, Nutrisi This applied research aims to build a model of determining the best packaged milk with consideration variables are price and nutrition. The steps used in this research are fuzzification, fuzzy rule determination, fuzzy inference with mamdani method, and defuzzification. The data used are data taken from direct field surveys conducted by researchers in one of the supermarkets in Makassar. The results of this study is sample 16 packaged milk which is the most suitable packaged milk to recommended because it has high nutrition and affordable prices.Keywords: Fuzzy logic, Mamdani, Milk, Price, Nutritio

Intelligent Computing in Medical Ultrasonic System

兵庫県立大学大学院201

Applications of artificial intelligence in dentistry: A comprehensive review

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Projects RTI2018-101674-B-I00 and PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, I+D+I Junta de Andalucia 2020 project P20-00200, and Fapergs/Capes do Brasil grant 19/25510000928-3. Funding for open-access charge: Universidad de Granada/CBUAObjective: 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.Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00 PGC2018-101904-A-100University of Granada project A.TEP. 280.UGR18Junta de Andalucia P20-00200Fapergs/Capes do Brasil grant 19/25510000928-3Universidad de Granada/CBU

Deteksi osteoporosis melalui analisis tekstur citra tulang manus pada wanita pasca menopause dengan menggunakan metode ekstraksi fitur Gray Level Run Length Matrix (GLRLM) dan klasifikasi Adaptive Neuro Fuzzy Inference System (ANFIS)

INDONESIA: Tulang merupakan jaringan dinamis yang mengalami perombakan sepanjang kehidupan, dimana sel-sel yang sudah tua dibongkar kemudian dibentuk sel yang baru. Abnormalitas yang terjadi pada remodeling tulang yaitu dimana proses pembongkaran (resorpsi) terjadi lebih cepat secara tidak teratur daripada pembentukan tulang (formasi) sehingga dapat menyebabkan terjadinya osteoporosis. Adapun biaya pemeriksaan BMD menggunakan DEXA yang cukup mahal terutama bagi masyarakat dengan kelas ekonomi menengah ke bawah serta penyebaran alat DEXA scanner yang tidak merata pada semua wilayah rumah sakit di Indonesia menyebabkan kurangnya monitoring dan perhatian masyarakat terhadap osteoporosis. Untuk menangani permasalahan tersebut, dilakukan upaya alternatif yang lebih cepat dan terjangkau dalam memonitoring dan mendeteksi osteoporosis menggunakan analisis citra tulang. Citra tulang terkait akan diekstraksi fitur teksturnya menggunakan Gray Level Run Length Matrix (GLRLM) dan diklasifikasikan melalui Adaptive Neuro Fuzzy Inference System (ANFIS). Penelitian ini bertujuan untuk mengetahui kinerja sistem prediksi GLRLM-ANFIS dalam mendeteksi dan mengklasifikasikan jenis tulang, untuk mengetahui sebaran validitas sistem GLRLM-ANFIS serta untuk mengetahui karakteristik fisik tekstur yang dihasilkan pada masing-masing citra tulang (normal; osteopenia; osteoporosis). Hasil pada penelitian menunjukkan bahwa sistem GLRLM-ANFIS memiliki kinerja sistem prediksi yang sangat baik dibuktikan dengan persentase error yang sangat rendah yaitu 0.120073078% pada data training dan 1.723160606% pada data testing, dengan sebaran validitas mencapai 100% pada keseluruhan parameter (akurasi, presisi, recall, spesivisitas dan F1 Score). Selain itu karakteristik citra kelas tulang normal memiliki permukaan citra yang halus, citra kelas osteoporosis memiliki permukaan citra yang kasar, sedangkan kelas osteopenia berada di antara keduanya. ENGLISH: Bone is a dynamic tissue that undergoes reshuffle throughout life, where old cells are dismantled and new cells are formed. Abnormalities that occur in bone remodeling, namely where the process of disassembly (resorption) occurs irregularly faster than bone formation so that it can cause osteoporosis. The cost of BMD examination using DEXA is quite expensive, especially for people with the middle to lower economic class and the distribution of DEXA scanners that are not evenly distributed in all areas of hospitals in Indonesia causes a lack of monitoring and public attention to osteoporosis. To deal with these problems, alternative efforts are made that are faster and more affordable in monitoring and detecting osteoporosis using bone image analysis. The associated bone images will be extracted for their texture features using the Gray Level Run Length Matrix (GLRLM) and classified through the Adaptive Neuro Fuzzy Inference System (ANFIS). This study aims to determine the performance of the GLRLM-ANFIS prediction system in detecting and classifying bone types, to determine the validity distribution of the GLRLM-ANFIS system and to determine the physical characteristics of the resulting texture on each bone image (normal; osteopenia; osteoporosis). The results of the study show that the GLRLM-ANFIS system has a very good prediction system performance as evidenced by a very low error percentage of 0.120073078% on training data and 1.723160606% on testing data, with a validity distribution reaching 100% on all parameters (accuracy, precision, recall, specificity and F1 score). Besides that, the normal bone class image characteristics have a smooth image surface, the osteoporosis class image has a rough image surface, while the osteopenia class is in between the two. ARABIC: العظام نسيج ديناميكي يخضع لتعديل وزاري طوال الحياة ، حيث يتم تفكيك الخاليا القديمة وتشكيل خاليا جديدة .التشوهات التي تحدث في إعادة تشكيل العظام ، أي حيث تحدث عملية التفكيك )االرتشاف (بشكل غير منتظم بشكل أسرع من تكوين العظام )تكوينها (بحيث يمكن أن تسبب هشاشة العظام .تكلفة فحص كثافة باهظة الثمن ، خاصة لألشخاص من الطبقة االقتصادية المتوسطة إلى DEXA المعادن بالعظام باستخدام غير الموزعة بالتساوي في جميع مناطق المستشفيات في إندونيسيا DEXA الدنيا ، كما أن توزيع ماسحات يتسبب في نقص المراقبة واالهتمام العام بهشاشة العظام .للتعامل مع هذه المشاكل ، تُبذل جهود بديلة أسرع وبأسعار معقولة في مراقبة واكتشاف هشاشة العظام باستخدام تحليل صورة العظام .سيتم استخراج صور وتصنيفها (GLRLM (العظام المصاحبة لميزات نسيجها باستخدام مصفوفة طول تشغيل المستوى الرمادي تهدف هذه الدراسة إلى تحديد أداء نظام التنبؤ (ANFIS (من خالل نظام االستدالل العصبي الغامض. التكيفي وتحديد الخصائص الفيزيائية للنسيج الناتج على كل صورة عظم )طبيعية ؛ هشاشة العظام ؛ هشاشة ANFIS GLRLM في الكشف عن أنواع العظام وتصنيفها ، لتحديد توزيع صالحية نظام ANFIS-GLRLM له أداء نظام تنبؤ جيد جدًا كما يتضح من نسبة ANFIS-GLRLM العظام .(أظهرت نتائج الدراسة أن نظام على بيانات االختبار %۱۷۲۳۱۶۰۶۰۶ على بيانات التدريب و .,%۰۱۲۰۰۷۳۰۷۸ خطأ منخفضة جدًا تبلغ على جميع المعلمات) .الدقة ، الدقة ، االسترجاع ، الخصوصية ، درجة %۱۰۰ مع توزيع صحة يصل إلى ، إلى جانب ذلك ، تتميز خصائص صورة فئة العظام الطبيعية بسطح صورة أملس ، وصورة فئة هشاشة .(1F .العظام لها سطح صورة خشن ، بينما تقع فئة هشاشة العظام بين االثني

Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

BACKGROUND: It is known that bone mineral density (BMD) predicts the fracture's risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF). Spinal deformity index (SDI) integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs) could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR) in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0. METHODOLOGY: We compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51), using 45 variables (44 clinical parameters plus BMD). ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion). Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first) and SDI≥5 vs SDI = 0 (second) analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively. CONCLUSIONS: ANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF

Fuzzy decision support systems to diagnose musculoskeletal disorders: A systematic literature review

Abstract Background and objective Musculoskeletal disorders (MSDs) are one of the most important causes of disability with a high prevalence. The accurate and timely diagnosis of these disorders is often difficult. Clinical decision support systems (CDSSs) can help physicians to diagnose diseases quickly and accurately. Given the ambiguous nature of MSDs, fuzzy logic can be helpful in designing the CDSSs knowledge bases. The present study aimed to review the studies on fuzzy CDSSs to diagnose MSDs. Methods A comprehensive search was conducted in Medline, Scopus, Cochrane Library, and ISI Web of Science databases to identify relevant studies published until March 15, 2016. Studies were included in which CDSSs were developed using fuzzy logic to diagnose MSDs, and tested their accuracy using real data from patients. Results Of the 3188 papers examined, 23 papers included according to the inclusion criteria. The results showed that among all the designed CDSSs only one (CADIAG-2) was implemented in the clinical environment. In about half of the included studies (52%), CDSSs were designed to diagnose inflammatory/infectious disorder of the bone and joint. In most of the included studies (70%), the knowledge was extracted using a combination of three methods (acquiring from experts, analyzing the data, and reviewing the literature). The median accuracy of fuzzy rule-based CDSSs was 91% and it was 90% for other fuzzy models. The most frequently used membership functions were triangular and trapezoidal functions, and the most used method for inference was the Mamdani. Conclusions In general, fuzzy CDSSs have a high accuracy to diagnose MSDs. Despite the high accuracy, these systems have been used to a limited extent in the clinical environments. To design of knowledge base for CDSSs to diagnose MSDs, rule-based methods are used more than other fuzzy methods. Keywords Musculoskeletal disorders Decision support systems Fuzzy logic Diagnose Revie

Opportunistic hip fracture risk prediction in Men from X-ray: Findings from the Osteoporosis in Men (MrOS) Study

Osteoporosis is a common disease that increases fracture risk. Hip fractures, especially in elderly people, lead to increased morbidity, decreased quality of life and increased mortality. Being a silent disease before fracture, osteoporosis often remains undiagnosed and untreated. Areal bone mineral density (aBMD) assessed by dual-energy X-ray absorptiometry (DXA) is the gold-standard method for osteoporosis diagnosis and hence also for future fracture prediction (prognostic). However, the required special equipment is not broadly available everywhere, in particular not to patients in developing countries. We propose a deep learning classification model (FORM) that can directly predict hip fracture risk from either plain radiographs (X-ray) or 2D projection images of computed tomography (CT) data. Our method is fully automated and therefore well suited for opportunistic screening settings, identifying high risk patients in a broader population without additional screening. FORM was trained and evaluated on X-rays and CT projections from the Osteoporosis in Men (MrOS) study. 3108 X-rays (89 incident hip fractures) or 2150 CTs (80 incident hip fractures) with a 80/20 split were used. We show that FORM can correctly predict the 10-year hip fracture risk with a validation AUC of 81.44 +- 3.11% / 81.04 +- 5.54% (mean +- STD) including additional information like age, BMI, fall history and health background across a 5-fold cross validation on the X-ray and CT cohort, respectively. Our approach significantly (p < 0.01) outperforms previous methods like Cox Proportional-Hazards Model and \frax with 70.19 +- 6.58 and 74.72 +- 7.21 respectively on the X-ray cohort. Our model outperform on both cohorts hip aBMD based predictions. We are confident that FORM can contribute on improving osteoporosis diagnosis at an early stage.Comment: Accepted at MICCAI 2022 Workshop (PRIME