Yonsei University Severance Hospital
Seoul, 03722, South Korea
NCT Number: NCT07287111
This is a retrospective, fully-crossed, multi-reader, multi-case (MRMC) study to evaluate the effectiveness of 'CadAI-B Dx' (CadAI-B) for decision support in breast ultrasound. The study compares the diagnostic performance of readers interpreting breast ultrasound images with and without the aid of CadAI-B. A total of 797 patient cases will be included, comprising 350 cases with a confirmed diagnosis of malignancy and 447 cases with a confirmed benign diagnosis. Sixteen readers will participate in the study to evaluate the device.
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Notify Me22 year and older
Female
Interventional
Not applicable
Seoul, 03722, South Korea
The study utilizes a crossover design where all readers independently review all cases. The control arm consists of a reading session where participating readers independently review cases without the assistance of the CadAI-B device (unaided reading). The experimental arm involves reading with CadAI-B assistance (AI-aided reading). To minimize potential bias, a washout period of four weeks will be maintained between the unassisted and assisted reading sessions for each reader. The primary hypothesis is that CadAI-B assistance significantly improves overall reader performance in breast ultrasound interpretation, as measured by the area under the Localization Receiver Operating Characteristic (LROC) curve (AULROC).
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
3-1. Malignant Cases: Diagnosis of breast cancer confirmed through biopsy or surgery.
3-2. Benign Cases: Confirmed as benign through biopsy or surgical excision, or confirmed as having no evidence of malignancy for at least 2 years of follow-up.
Exclusion criteria
CadAI-B Dx is a Software as a Medical Device (SaMD) designed to assist physicians by providing Computer-Aided Detection (CADe) and Diagnosis (CADx) capabilities in breast ultrasound interpretation. The software automatically processes the image to identify suspicious regions (Lesion Detection) and provides a quantitative malignancy score (CadAI-Score) mapped to a corresponding BI-RADS Category. It also analyzes lesion size and BI-RADS lexicon descriptors.
Time frame: Through study completion, approximately 2 months
The difference in reader performance between the unaided and AI-aided sessions in breast ultrasound interpretation. LROC reflects both detection accuracy and localization precision. The primary hypothesis is that the mean AULROC of all readers for the AI-aided reading mode is greater than that for the unaided reading mode.
Time frame: Through study completion, approximately 2 months
Comparison of the average sensitivity of the readers between the unaided and AI-aided sessions.
Time frame: Through study completion, approximately 2 months
Comparison of PPV between unaided and AI-aided sessions. The PPV will be adjusted for disease prevalence in the target population to reflect real-world clinical practice.
Time frame: Through study completion, approximately 2 months
Comparison of NPV between unaided and AI-aided sessions. The NPV will be adjusted for disease prevalence in the target population.
Time frame: Through study completion, approximately 2 months
Evaluation of the consistency of interpretations among different readers. Inter-reader agreement for BI-RADS category and descriptors assignments will be compared between sessions using Kappa statistics.
Time frame: Through study completion, approximately 2 months
Comparison of the average reading time per case between the unaided and AI-aided sessions to assess if the AI system improves the efficiency of interpretation.
Time frame: Through study completion, approximately 2 months
Assessment of the agreement between the AI system's outputs and the ground truth. This includes agreement on BI-RADS categories and descriptors (using Kappa statistics) and lesion size measurements (using Intraclass Correlation Coefficient).
BeamWorks Inc.
Industry
Multi-Reader Multi-Case, Cross-Over, Retrospective Study to Evaluate the Effectiveness of CadAI-B Dx for Decision Support in Breast Ultrasound
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