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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.
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).
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| CadAI- Dx(AI) Unaided Reading | No Intervention | Readers evaluate each ultrasound image without access to AI assistance. Readers use only the provided radiological images and their professional clinical expertise to determine their final diagnosis and relevant findings | |
| CadAI-DX (AI)-aided Reading | Experimental | Readers have access to CadAI-B's decision-support outputs, which include a pixel-level heatmap (CadAI-Map), auto-segmented lesion boundary, BI-RADS lexicon descriptors, a suggested malignancy probability score (CadAI-Score), and BI-RADS category. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CadAI-B Dx | Device | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Area Under the Localization Receiver Operating Characteristic (LROC) Curve (AULROC) | 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. | Through study completion, approximately 2 months |
| Measure | Description | Time Frame |
|---|---|---|
| Sensitivity | Comparison of the average sensitivity of the readers between the unaided and AI-aided sessions. | Through study completion, approximately 2 months |
| Positive Predictive Value (PPV) | 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. |
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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:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Yonsei University Severance Hospital | Seoul | 03722 | South Korea |
ndividual participant data will not be shared to protect proprietary information and subject confidentiality.
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| ID | Term |
|---|---|
| D001943 | Breast Neoplasms |
| ID | Term |
|---|---|
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001941 | Breast Diseases |
| D012871 | Skin Diseases |
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Retrospective, fully-crossed, Multi-Reader Multi-Case (MRMC) study.
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| Through study completion, approximately 2 months |
| Negative Predictive Value (NPV) | Comparison of NPV between unaided and AI-aided sessions. The NPV will be adjusted for disease prevalence in the target population. | Through study completion, approximately 2 months |
| Inter-reader Agreement | 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. | Through study completion, approximately 2 months |
| Reading Time | 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. | Through study completion, approximately 2 months |
| AI-Ground Truth Agreement | 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). | Through study completion, approximately 2 months |
| D017437 |
| Skin and Connective Tissue Diseases |