Automated Risk Identification of Myocardial Infarction Using Relative Frequency Band Coefficient (RFBC) Features from ECG

Various structural and functional changes associated with ischemic (myocardial infarcted) heart cause amplitude and spectral changes in signals obtained at different leads of ECG. In order to capture these changes, Relative Frequency Band Coefficient (RFBC) features from 12-lead ECG have been proposed and used for automated identification of myocardial infarction risk. RFBC features reduces the effect of subject variabilty in body composition on the amplitude dependent features. The proposed method is evaluated on ECG data from PTB diagnostic database using support vector machine as classifier. The promising result suggests that the proposed RFBC features may be used in the screening and clinical decision support system for myocardial infarction.

Multilevel Medical Image Fusion using Segmented Image by Level Set Evolution with Region Competition

In this paper, a region level based image fusion technique, using wavelet transform, has been implemented and analyzed. The proposed methodology considers regions as the basic feature for representing images and uses region properties for extracting the information from them. A segmentation algorithm is proposed for extracting the regions in an effective way for fusing the images. The fusion strategy uses multi - level decomposition of the images obtained using wavelet transform. By analyzing the images at multiple levels, the proposed method is able to extract finer details from them and in turn improves the quality of the fused image. The performance and relative importance of the proposed methodology is investigated using the Mutual Information criteria. Experimental Results show that the proposed method improves the quality of the fused image significantly for both the normal and multifocused images.

Telemedicine interventions for improving antibiotic stewardship and prescribing: A systematic review

The global antibiotic resistance crisis necessitates optimized stewardship programs, with telemedicine emerging as a promising delivery strategy. This systematic review evaluated the effectiveness of telemedicine interventions in improving antibiotic stewardship across clinical settings. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we systematically searched seven databases from January 2010 to July 2024. Two independent reviewers assessed studies using Risk of Bias in Non-randomized Studies (ROBINS-I) and Cochrane Risk of Bias 2.0 tools, with evidence certainty evaluated using Grading of Recommendations Assessment, Development, and Evaluation (GRADE). Twenty-one studies met inclusion criteria (10 observational, 8 quasi-experimental, 2 Randomized Controlled Trials [RCTs], 1 mixed-methods), predominantly from the United States (57.0%, n = 12). Among studies reporting antibiotic use outcomes (52.4%, n = 11), 90.9% demonstrated significant reductions ranging from 5.3% to 62.7%, with the highest-quality evidence showing a 28% reduction (95% Confidence Interval [CI]: 22-34%). Guideline adherence studies (57.1%, n = 12) showed acceptance rates of 67.7% to 98%, with comparable effectiveness between telemedicine and in-person consultation (79.1% vs 80.4%, p = 0.36). Prescribing rate outcomes (38.1%, n = 8) revealed setting-dependent variations: inpatient implementations demonstrated significant reductions (Relative Risk [RR] 0.68; 95% CI: 0.63-0.75), while outpatient findings showed mixed results. Quality assessment revealed predominantly low risk of bias [ROB] (66.7%, n = 14). These findings suggest that telemedicine effectively improves antibiotic stewardship compared to traditional care models, particularly in hospital settings, while outpatient applications demonstrated variable effectiveness. This review was registered with the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023454663).