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.
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http://dx.doi.org/10.1007/s00330-010-1821-8 | DOI Listing |
Dent Mater
January 2025
Department of Oral Technology, Medical Faculty, University Hospital Bonn, Bonn, North Rhine-Westphalia, Germany; Department of Fixed Prosthodontics, Faculty of Dentistry, Suez Canal University, Ismailia, Egypt.
Objectives: To compare the flexural strength and modulus of denture base resins manufactured by conventional methods, 3-dimensional (3D) printing, and computer-aided design and computer-aided manufacturing (CAD/CAM) milling using 3-point bending (3PB) and 4-point bending (4PB) methods after simulated aging.
Methods: Ninety bars (64 ×10 ×3.3 mm) were prepared from heat-polymerized (Lucitone-199), CAD/CAM milled (G-CAM), and 3D-printed (Denturetec) denture base resins (n = 30 per material).
Mol Carcinog
January 2025
Key Laboratory of Computer-Aided Drug Design of Dongguan City, The First Dongguan Affiliated Hospital, School of Pharmacy, Guangdong Medical University, Dongguan, Guangdong, China.
The progression of tumors has been demonstrated to have a strong correlation with ferroptosis. Bis(4-hydroxy-3,5-dimethylphenyl) sulfone (TMBPS) has been shown to effectively inhibit the proliferation of hepatocellular carcinoma (HCC), but its underlying mechanism is not clear. In this study, ferrostatin-1 (Fer-1) was employed to explore whether the death of HCC cells caused by TMBPS is related to ferroptosis.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Background: Pneumonia is an acute respiratory infection that has emerged as the predominant catalyst for escalating mortality rates worldwide. In the pursuit of the prevention and prediction of pneumonia, this work employs the development of an advanced deep-learning model by using a federated learning framework. The deep learning models rely on the utilization of a centralized system for disease prediction on the medical imaging data and pose risks of data breaches and exploitation; however, federated learning is a decentralized architecture which significantly reduces data privacy concerns.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Gastroenterology Department of Gandhi Medical College, Bhopal, 462003, India.
Gastrointestinal tract-related cancers pose a significant health burden, with high mortality rates. In order to detect the anomalies of the gastrointestinal tract that may progress to cancer, a video capsule endoscopy procedure is employed. The number of video capsule endoscopic ( ) images produced per examination is enormous, which necessitates hours of analysis by clinicians.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science, National Textile University, Faisalabad, Pakistan.
Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems.
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