ArticleDiagnostics (Basel, Switzerland)2022
Evaluation of Computer-Aided Detection (CAD) in Screening Automated Breast Ultrasound Based on Characteristics of CAD Marks and False-Positive Marks.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Analysis of effectiveness in an artificial intelligent film reading system combined with liquid based cytology examination for cervical cancer screening.American journal of translational research · 2024Article
- Expanding Horizons: The Realities of CAD, the Promise of Artificial Intelligence, and Machine Learning's Role in Breast Imaging beyond Screening Mammography.Diagnostics (Basel, Switzerland) · 2023Review
- A New Deep-Learning-Based Model for Breast Cancer Diagnosis from Medical Images.Diagnostics (Basel, Switzerland) · 2023Article
- Ultrasound for Breast Cancer Screening in Resource-Limited Settings: Current Practice and Future Directions.Cancers · 2023Review
- The Systematic Review of Artificial Intelligence Applications in Breast Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2022Review
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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Abstract
The present study evaluated the effectiveness of computer-aided detection (CAD) system in screening automated breast ultrasound (ABUS) and analyzed the characteristics of CAD marks and the causes of false-positive marks. A total of 846 women who underwent ABUS for screening from January 2017 to December 2017 were included. Commercial CAD was used in all ABUS examinations, and its diagnostic performance and efficacy in shortening the reading time (RT) were evaluated. In addition, we analyzed the characteristics of CAD marks and the causes of false-positive marks. A total of 1032 CAD marks were displayed based on the patient and 534 CAD marks on the lesion. Five cases of breast cancer were diagnosed. The sensitivity, specificity, PPV, and NPV of CAD were 60.0%, 59.0%, 0.9%, and 99.6% for 846 patients. In the case of a negative study, it was less time-consuming and easier to make a decision. Among 530 false-positive marks, 459 were identified clearly for pseudo-lesions; the most common cause was marginal shadowing, followed by Cooper's ligament shadowing, peri-areolar shadowing, rib, and skin lesions. Even though CAD does not improve the performance of ABUS and a large number of false-positive marks were detected, the addition of CAD reduces RT, especially in the case of negative screening ultrasound.
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