Computer aided detection in mammography

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Evaluating computer‐aided detection algorithms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134784/1/mp6289.pd

Computer-aided detection of pulmonary nodules in low-dose CT

A computer-aided detection (CAD) system for the identification of pulmonary nodules in low-dose multi-detector helical CT images with 1.25 mm slice thickness is being developed in the framework of the INFN-supported MAGIC-5 Italian project. The basic modules of our lung-CAD system, a dot enhancement filter for nodule candidate selection and a voxel-based neural classifier for false-positive finding reduction, are described. Preliminary results obtained on the so-far collected database of lung CT scans are discussed.Comment: 3 pages, 4 figures; Proceedings of the CompIMAGE - International Symposium on Computational Modelling of Objects Represented in Images: Fundamentals, Methods and Applications, 20-21 Oct. 2006, Coimbra, Portuga

Role of Artificial Intelligence in Colonoscopy Detection of Advanced Neoplasias. A Randomized Trial.

Background: The role of computer-aided detection in identifying advanced colorectal neoplasia is unknown. Objective: To evaluate the contribution of computer-aided detection to colonoscopic detection of advanced colorectal neoplasias as well as adenomas, serrated polyps, and nonpolypoid and right-sided lesions. Design: Multicenter, parallel, randomized controlled trial. (ClinicalTrials.gov: NCT04673136) Setting: Spanish colorectal cancer screening program. Participants: 3213 persons with a positive fecal immunochemical test. Intervention: Enrollees were randomly assigned to colonoscopy with or without computer-aided detection. Measurements: Advanced colorectal neoplasia was defined as advanced adenoma and/or advanced serrated polyp. Results: The 2 comparison groups showed no significant difference in advanced colorectal neoplasia detection rate (34.8% with intervention vs. 34.6% for controls; adjusted risk ratio [aRR], 1.01 [95% CI, 0.92 to 1.10]) or the mean number of advanced colorectal neoplasias detected per colonoscopy (0.54 [SD, 0.95] with intervention vs. 0.52 [SD, 0.95] for controls; adjusted rate ratio, 1.04 [99.9% CI, 0.88 to 1.22]). Adenoma detection rate also did not differ (64.2% with intervention vs. 62.0% for controls; aRR, 1.06 [99.9% CI, 0.91 to 1.23]). Computer-aided detection increased the mean number of nonpolypoid lesions (0.56 [SD, 1.25] vs. 0.47 [SD, 1.18] for controls; adjusted rate ratio, 1.19 [99.9% CI, 1.01 to 1.41]), proximal adenomas (0.94 [SD, 1.62] vs. 0.81 [SD, 1.52] for controls; adjusted rate ratio, 1.17 [99.9% CI, 1.03 to 1.33]), and lesions of 5 mm or smaller (polyps in general and adenomas and serrated lesions in particular) detected per colonoscopy. Limitations: The high adenoma detection rate in the control group may limit the generalizability of the findings to endoscopists with low detection rates. Conclusion: Computer-aided detection did not improve colonoscopic identification of advanced colorectal neoplasias. Primary Funding Source: Medtronic

Erratum: “Evaluating computer‐aided detection algorithms”

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134826/1/mp5750.pd