98%
921
2 minutes
20
Objective: To evaluate an interactive computer-aided detection (CAD) system for reading mammograms to improve decision making.
Methods: A dedicated mammographic workstation has been developed in which readers can probe image locations for the presence of CAD information. If present, CAD findings are displayed with the computed malignancy rating. A reader study was conducted in which four screening radiologists and five non-radiologists participated to study the effect of this system on detection performance. The participants read 120 cases of which 40 cases had a malignant mass that was missed at the original screening. The readers read each mammogram both with and without CAD in separate sessions. Each reader reported localized findings and assigned a malignancy score per finding. Mean sensitivity was computed in an interval of false-positive fractions less than 10%.
Results: Mean sensitivity was 25.1% in the sessions without CAD and 34.8% in the CAD-assisted sessions. The increase in detection performance was significant (p = 0.012). Average reading time was 84.7 ± 61.5 s/case in the unaided sessions and was not significantly higher when interactive CAD was used (85.9 ± 57.8 s/case).
Conclusion: Interactive use of CAD in mammography may be more effective than traditional CAD for improving mass detection without affecting reading time.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2940044 | PMC |
http://dx.doi.org/10.1007/s00330-010-1821-8 | DOI Listing |
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.
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 PDFDig 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).
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 PDFRep 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.