Assessment of Radiation Exposure Dose for Nuclear Medicine Workers from 18F-FDG, 99mTc MDP, and 99mTc.

BACKGROUND: Nuclear medicine or diagnostic radiology personnel are always exposed to low-level radiation from radionuclides used in medical diagnostics, which lead to potential biological hazards or effects. OBJECTIVE: External exposure for workers in two nuclear medicine centers was measured by recruiting 120 patients. METHODS: Three nuclear medicine examinations were performed using F18-FDG PET/CT,99mTc- MDP bones scan, and 99mTc thyroid scan by a digital radiation dosimeter. RESULTS: The average received accumulative radiation dose for workers was found to be 0.838±0.17, 0.527±0.11, and 0.270±0.05 µSv for F18-FDG PET/CT, 99mTc-MDP bones scan, and 99mTc thyroid scan, respectively. The annual effective dose for workers was estimated to be 2.09±0.42, 1.34±0.27, and 0.68±0.14 mSv, respectively. Moreover, the average patient-to-staff dose coefficients were found to be 0.024±0.005, 0.003±0.001, and 0.007±0.002 µSv m2/MBq h for F18- FDG PET/CT, 99mTc-MDP bones scan, and 99mTc thyroid scan, respectively. CONCLUSION: It is clear from the results that the radiation doses received by workers during the nuclear medicine imaging examinations were less than the doses recommended by the International Commission on Radiological Protection (ICRP) for such examinations.

A Clinical Decision Support System for Diabetic Retinopathy Screening: Creating a Clinical Support Application.

BACKGROUND: The aim of this study was to build a clinical decision support system (CDSS) in diabetic retinopathy (DR), based on type 2 diabetes mellitus (DM) patients. METHOD: We built a CDSS from a sample of 2,323 patients, divided into a training set of 1,212 patients, and a testing set of 1,111 patients. The CDSS is based on a fuzzy random forest, which is a set of fuzzy decision trees. A fuzzy decision tree is a hierarchical data structure that classifies a patient into several classes to some level, depending on the values that the patient presents in the attributes related to the DR risk factors. Each node of the tree is an attribute, and each branch of the node is related to a possible value of the attribute. The leaves of the tree link the patient to a particular class (DR, no DR). RESULTS: A CDSS was built with 200 trees in the forest and three variables at each node. Accuracy of the CDSS was 80.76%, sensitivity was 80.67%, and specificity was 85.96%. Applied variables were current age, gender, DM duration and treatment, arterial hypertension, body mass index, HbA1c, estimated glomerular filtration rate, and microalbuminuria. DISCUSSION: Some studies concluded that screening every 3 years was cost effective, but did not personalize risk factors. In this study, the random forest test using fuzzy rules permit us to build a personalized CDSS. CONCLUSIONS: We have developed a CDSS that can help in screening diabetic retinopathy programs, despite our results more testing is essential.

Learning ensemble classifiers for diabetic retinopathy assessment.

Diabetic retinopathy is one of the most common comorbidities of diabetes. Unfortunately, the recommended annual screening of the eye fundus of diabetic patients is too resource-consuming. Therefore, it is necessary to develop tools that may help doctors to determine the risk of each patient to attain this condition, so that patients with a low risk may be screened less frequently and the use of resources can be improved. This paper explores the use of two kinds of ensemble classifiers learned from data: fuzzy random forest and dominance-based rough set balanced rule ensemble. These classifiers use a small set of attributes which represent main risk factors to determine whether a patient is in risk of developing diabetic retinopathy. The levels of specificity and sensitivity obtained in the presented study are over 80%. This study is thus a first successful step towards the construction of a personalized decision support system that could help physicians in daily clinical practice.