Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Computer-aided detection algorithms based on artificial intelligence are increasingly being tested and used as a means for detecting tuberculosis in countries where the epidemic is still present. Computer-aided detection tools are often presented as a global solution that can be deployed in all the geographical areas concerned by tuberculosis, but at the same time, they need to be adjusted and calibrated according to local populations' characteristics. The aim of this article is to analyze the tensions between the standardization of computer-aided detection algorithms and their local adaptation and the political issues associated with these tensions. We undertook a qualitative analysis of practices associated with tuberculosis detection algorithms in different contexts, contrasting the perspectives of various stakeholders. Algorithms embed the promise of standardization through automation and the bypassing of variable human expertise such as that of radiologists, they are nonetheless objects of local practices that we have characterized as "tweaking." This work of tweaking reveals how the technology is situated but also the many concerns of the users and workers (insertion in care, control over infrastructure, and political ownership). This should be better considered to truly make computer-aided detection innovative tools for tuberculosis management in global health.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11020726 | PMC |
| http://dx.doi.org/10.1177/20552076241239778 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
High-resolution imaging system for integration into intelligent noncontact total body scanner.
J Biomed Opt
September 2025
Leibniz University Hannover, Hannover Centre for Optical Technologies, Hannover, Germany.
Significance: Melanoma's rising incidence demands automatable high-throughput approaches for early detection such as total body scanners, integrated with computer-aided diagnosis. High-quality input data is necessary to improve diagnostic accuracy and reliability.
Aim: This work aims to develop a high-resolution optical skin imaging module and the software for acquiring and processing raw image data into high-resolution dermoscopic images using a focus stacking approach.
Advanced multimodal for Segmentation and Classification of breast cancer with DenseNet and Optimal Attention approach.
Comput Methods Programs Biomed
August 2025
Zhengzhou University, School of Computer and Artificial Intelligence, Zhengzhou, 450001, China. Electronic address:
Background And Objective: The early detection of breast cancer plays a critical role in improving survival rates and facilitating precise medical interventions. Therefore, the automated identification of breast abnormalities becomes paramount, significantly enhancing the prospects of successful treatment outcomes. To address this imperative, our research leverages multiple modalities such as MRI, CT, and mammography to detect and screen for breast cancer.
View Article and Find Full Text PDFBowel preparation quality and computer-aided detection of adenomas during colonoscopy.
Dig Liver Dis
September 2025
Department of Gastroenterology, Valduce Hospital, Como, Italy. Electronic address:
Objectives: Computer-aided detection (CADe) systems improve adenoma detection during colonoscopy, but the influence of bowel preparation quality on CADe performance is unclear. This study assessed whether different levels of adequate bowel preparation affect CADe effectiveness.
Methods: A post-hoc pooled analysis was conducted using individual patient data from three randomized controlled trials comparing CADe-assisted colonoscopy to standard colonoscopy (SC).
Automated segmentation of retinal vessel using HarDNet fully convolutional networks.
PLoS One
September 2025
School of Medical Engineering, Xinxiang Medical University, Xinxiang, China.
Computer-aided diagnostic (CAD) systems for color fundus images play a critical role in the early detection of fundus diseases, including diabetes, hypertension, and cerebrovascular disorders. Although deep learning has substantially advanced automatic segmentation techniques in this field, several challenges persist, such as limited labeled datasets, significant structural variations in blood vessels, and persistent dataset discrepancies, which continue to hinder progress. These challenges lead to inconsistent segmentation performance, particularly for small vessels and branch regions.
View Article and Find Full Text PDFOptimizing cervical cancer diagnosis with accurate cell classification using modified HDFF.
Rep Pract Oncol Radiother
August 2025
University Teaching Department, Chhattisgarh Swami Vivekanand Technical University, Bhilai, India.
Background: Cervical cancer (CC) is a leading cause of cancer-related deaths worldwide, emphasizing the need for accurate and efficient diagnostic tools. Traditional methods of cervical cell classification are time-consuming and susceptible to human error, highlighting the need for automated solutions.
Materials And Methods: This study introduces the modified hierarchical deep feature fusion (HDFF) method for cervical cell classification using the SIPaKMeD and Herlev datasets.