مشخصات مقاله | |
ترجمه عنوان مقاله | استفاده از هوش مصنوعی برای شناسایی افزایش خطر ابتلا به پوکی استخوان در زنان یائسه |
عنوان انگلیسی مقاله | Use of Artificial Intelligence for Identification of Increased Risk of Osteoporosis Development in Postmenopausal Women |
نشریه | آی تریپل ای – IEEE |
سال انتشار | ۲۰۲۲ |
تعداد صفحات مقاله انگلیسی | ۴ صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
نوع نگارش مقاله |
مقاله پژوهشی (Research article) |
مقاله بیس | این مقاله بیس میباشد |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
شناسه ISSN | ۲۶۳۷-۹۵۱۱ |
فرضیه | ندارد |
مدل مفهومی | دارد |
پرسشنامه | ندارد |
متغیر | دارد |
رفرنس | دارد |
رشته های مرتبط | پزشکی – مهندسی کامپیوتر |
گرایش های مرتبط | جراحی ارتوپدی – زنان و زایمان – هوش مصنوعی |
نوع ارائه مقاله |
ژورنال – کنفرانسی |
مجله / کنفرانس | ۲۰۲۲ یازدهمین کنفرانس مدیترانه ای در محاسبات حک شده (MECO) – 2022 11th Mediterranean Conference on Embedded Computing (MECO) |
دانشگاه | Faculty of Pharmacy, University of Sarajevo, Sarajevo, Bosnia and Herzegovina |
کلمات کلیدی | پوکی استخوان – ارزیابی خطر – زنان یائسه – شبکه عصبی مصنوعی |
کلمات کلیدی انگلیسی | Osteoporosis – risk assessment – postmenopausal women – Artificial Neural Network |
شناسه دیجیتال – doi |
https://doi.org/10.1109/MECO55406.2022.9797117 |
لینک سایت مرجع |
https://ieeexplore.ieee.org/document/9797117 |
کد محصول | e17094 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
Abstract I. Introduction II. Methods III. Results and Discussion IV. Conclusion References |
بخشی از متن مقاله: |
Abstract This paper presents an Artificial Nerual Network (ANN) for identification of postmenopausal women who are at high risk for developing osteopathy. While 800 patients took part in the study, 180 were used for network training. The following parameters were used: T-score (from −۲,۵ to −۴), Age, Blood calcium level (<1,9 mmol/L), Blood vitamin D level (<20 ng/ml), Hip fracture, Spine fracture, Joint fracture, Glucocorticoids use, Smoking status, and BMI. The network has 10 input parameters and 1 output parameter. For the final architecture of expert system, a neural network with 20 neurons in hidden layer was chosen based on the training results. The signal from each neuron from hidden layer is directed to neuron in output layer, where this neuron processes the signal and gives desired output of the network. The sensitivity was 97,5%, specificity 70%, and accuracy 94,44%. Introduction Osteoporosis is a skeletal illness that causes weakening of the bones, which can lead to increased fractures. People with osteoporosis have decreased bone mass and microarchitectural degeneration of bone tissue, in addition to lower bone strength [1-3]. Osteoporosis is a skeletal illness that causes weakening of the bones, which can lead to increased fractures. People with osteoporosis have decreased bone mass and microarchitectural degeneration of bone tissue, in addition to lower bone strength [1-3]. According to data from 2010, 6,6% of men and 22,1% of women over 50 in the EU have osteoporosis. Because of the expanding number of patients worldwide, it is now referred to as a “silent epidemic.” Variable and non-variable factors that raise the chance of developing osteoporosis and bone fractures can be separated. One of the most important variables is smoking, which is linked to decreased bone resistance to mechanical stresses and friction. The effects of excessive alcohol use on bone homeostasis are significant. Caffeine, glucocorticoid therapy, insufficient calcium and vitamin D intake, insufficient physical activity, low BMI, past bone fractures, and a family history of osteoporosis are all risk factors [4-6]. Conclusion As osteoporosis in postmenopausal women is a rising problem in the modern world, predictive modelling of the risk of developing osteoprosis is highly desirable. This paper presents the development and validation of an ANN based model for prediction and automated diagnosis of osteoporosis in post-menopausal women based on risk-contributing factors. Giving the high accuracy and sensitivity of proposed ANN for identification of high risk of osteoporosis development in postmenopausal women, it can be concluded that AI has a high potential for decission making for this specific purpose. Prediction of high risk for osteoprosis development can contribute to adjustments in lifestyle and possible prevention of osteoporosis. In addition to the benefit this would have to each individual, the cost reduction in terms of preventing costly interventions necessary in case of osteoporosis development is a significant contribution. |