2 research outputs found
Validation of Deep Learning techniques for quality augmentation in diffusion MRI for clinical studies
The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise
Implementation of The Kanban and 5S Methodology in The Upstream Supply Chain of The Jute Industry: A Case Study in Bangladesh
<h2>Abstract</h2><p>Over the past few decades, Bangladesh, a South Asian nation at the nexus of economic dynamism and social advancement, has seen remarkable expansion in its export industry and income levels. Among all the sectors jute is one of them which has been playing quite a significant role in the export sector of Bangladesh. Bangladesh's jute and jute products have discovered markets for eco-friendly packaging materials in addition to conventional usage, boosting export earnings. But due to several issues, the jute industry is facing lots of problems nowadays. Local traders and farmers are suffering as a result of these issues. The upstream supply chain of jute plays a significant role in the export sector but the jute industry in Bangladesh has a number of serious problems in the upstream supply chain. These include seasonal variations in the availability of raw jute, outdated agricultural methods, inventory issues, and transportation infrastructure, problems with quality control, slow adoption of new technologies, financial issues, environmental concerns, and a lack of market information.</p><p>So this paper provides a thorough case study of the effective application of the Kanban and 5S techniques to the upstream supply chain of Bangladesh's jute industry. So improvement of the management of material flow and workstation organization by utilizing the Kanban and 5S concepts is the goal of this research. By creating a model for the local farmers and traders, this study adds to the corpus of knowledge on successful supply chain management techniques by demonstrating the beneficial synergy between Kanban and 5S in a practical setting.</p>