Search published articles


Showing 3 results for Artificial Intelligence
Breast cancer diagnosis by thermal imaging in the fields of medical and artificial intelligence sciences: review article
Hossein Ghayoumi Zadeh, Sogol Masoumzadeh, Shirin Nour, Sogol Kianersi, Zahra Eyvazi Zadeh, Farinaz Joneidi Shariat Zadeh, Javad Haddadnia, Farnoosh Khamseh, Nasrin Ahmadinejad,
Volume 74, Issue 6 (9-2016)
Abstract

Breast cancer is the most common cancer in women and one of the leading of death among them. The high and increasing incidence of the disease and its difficult treatment specifically in advanced stages, imposes hard situations for different countries’ health systems. Body temperature is a natural criteria for the diagnosis of diseases. In recent decades extensive research has been conducted to increase the use of thermal cameras and obtain a close relationship between heat and temperature of the skin's physiology. Thermal imaging (thermography) applies infrared method which is fast, non-invasive, non-contact and flexibile to monitor the temperature of the human body. This paper investigates highly diversified studies implemented before and after the year 2000. And it emphasizes mostly on the newely published articles including: performance and evaluation of thermal imaging, the various aspects of imaging as well as The available technology in this field and its disadvantages in the diagnosis of breast cancer. Thermal imaging has been adopted by researchers in the fields of medicine and biomedical engineering for the diagnosis of breast cancer. With the advent of modern infrared cameras, data acquisition and processing techniques, it is now possible to have real time high resolution thermographic images, which is likely to surge further research in this field.  Thermography does not provide information on the structures of the breast morphology, but it provides performance information of temperature and breast tissue vessels. It is assumed that the functional changes occured before the start of the structural changes which is the result of disease or cancer. These days, thermal imaging method has not been established as an applicative method for screening or diagnosing purposes in academic centers. But there are different centers that adopt this method for the diognosis and examining purposes. Thermal imaging is an effective method which is highly facilitative for breast cancer screening (due to the low cost and without harms), also, its impact will increase by combining other methods such as a mammogram and sonography. However, it has not been widely recognizesd as an accepted method for determineing the types of tumors (benign and malignant) and diseases of breast tissue.


Designing an intelligent system for diagnosing type 2 diabetes using the data mining approach: brief report
Rohollah Kalhor , Asghar Mortezagholi , Fatemeh Naji, Saeed Shahsavari, Mohammad Zakaria Kiaei ,
Volume 76, Issue 12 (3-2019)
Abstract
Background: Diabetes mellitus has several complications. The Late diagnosis of diabetes in people leads to the spread of complications. Therefore, this study has been done to determine the possibility of predicting diabetes type 2 by using data mining techniques.
Methods: This is a descriptive-analytic study that was conducted as a cross-sectional study. The study population included people referring to health centers in Mohammadieh City in Qazvin Province, Iran, from April to June 2015 for screening for diabetes. The 5-step CRISP method was used to implement this study. Data were collected from March 2015 to June 2015. In this study, 1055 persons with complete information were included in the study. Of these, 159 were healthy and 896 were diabetic. A total of 11 characteristics and risk factors were examined, including the age, sex, systolic and diastolic blood pressure, family history of diabetes, BMI, height, weight, waistline, hip circumference and diagnosis. The results obtained by support vector machine (SVM), decision tree (DT) and the k-nearest neighbors algorithm (k-NN) were compared with each other. Data was analyzed using MATLAB® software, version 3.2 (Mathworks Inc., Natick, MA, USA).
Results: Data analysis showed that in all criteria, the best results were obtained by decision tree with accuracy (0.96) and precision (0.89). The k-NN methods were followed by accuracy (0.96) and precision (0.83) and support vector machine with accuracy (0.94) and precision (0.85). Also, in this study, decision tree model obtained the highest degree of class accuracy for both diabetes classes and healthy in the analysis of confusion matrix.
Conclusion: Based on the results, the decision tree represents the best results in the class of test samples which can be recommended as a model for predicting diabetes type 2 using risk factor data.

Diagnosis of pneumothorax using machine learning algorithms and radiomics: Investigating the possibility of replacing simple chest radiography instead of CT scan in order to reduce the radiation dose
Hanieh Alimiri Dehbaghi , Karim Khoshgard, Hamid Sharini, Samira Jafari Khairabadi, Farhad Naleini,
Volume 81, Issue 5 (8-2023)
Abstract
Background: The use of artificial intelligence algorithms to help with accurate diagnosis in medical images is one of the most important applications of this technology in the field of medical imaging. In this research, the possibility of replacing simple chest radiography instead of CT scan using machine learning models to detect pneumothorax was investigated in cases where CT is usually requested.
Methods: This study is analytical and was conducted from November 2022 to May 2023 at Kermanshah University of Medical Sciences. The data used in this research was extracted from the files of 350 patients suspected of pneumothorax. The collected images were pre-processed in MATLAB software. Then, three machine learning algorithms, including Logistic elastic net regression (LENR), Logistic lasso regression (LLR) and Adaptive Boosting (AdaBoost) were used. To evaluate the performance of these models, the criteria of precision, accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), F1 score, and misclassification were used.
Results: In the AdaBoost model, the accuracy value in radiographic and CT images was calculated as 98.89% and 98.63%, respectively, and the precision value was calculated as 99.17% and 98.27%, respectively. In radiographic images, the AUC value for AdaBoost model was calculated as 100% and in CT scan images as 96.96%. The F1 score for the same model in radiographic was 99% and in CT images was 98.68%. The specificity value for the AdaBoost model was calculated as 99.45% in radiographic images and 94.67% in CT scan images. In the LLR model, the AUC value for radiographic and CT scan images was 99.87% and 99.02%, respectively.
Conclusion: According to the criteria evaluated in the present study, two LLR and AdaBoost models have similar performance in radiographic and CT images in terms of pneumothorax detection ability, so that this complication can also be diagnosed with high precision level using machine learning techniques on the radiographic images and thus receiving higher levels of radiation doses due to CT scan can be avoided in these patients.

Page 1 from 1     

© 2024 , Tehran University of Medical Sciences, CC BY-NC 4.0

Designed & Developed by : Yektaweb