Retrospective Multicenter Multi-Reader, Multi-Case Diagnostic Accuracy Study of Carebot AI MMG Compared With Radiologists on 2D Full-Field Digital Mammography in Breast Cancer Screening

Design and setting This is a retrospective, multicenter, multi-reader, multi-case (MRMC) diagnostic accuracy study of Carebot AI MMG, conducted on anonymized 2D full-field digital mammography (FFDM) examinations acquired as part of routine breast cancer screening. Mammograms were collected from four screening centers over a defined time period. No additional imaging was performed for the purpose of this study, and no subjects were contacted.

Data source and population The source dataset consists of 4,729 screening mammography examinations from women aged 32 to 88 years (mean approximately 57 years). Only 2D FFDM studies with a complete set of standard projections (LCC, RCC, LMLO, RMLO) were included. Examinations with incomplete series, unreadable or corrupted DICOM files, or missing/inconsistent key metadata were excluded, as were tomosynthesis (DBT) studies, men, and women under 18 years of age. To ensure sufficient precision of performance estimates, the dataset was enriched with additional biopsy-proven cancers. The final analytical subset comprises 222 examinations, including 48 malignant and 174 non-malignant studies, with representation across three mammography devices (Hologic Selenia Dimensions, Hologic Lorad Selenia, Fujifilm FDR-3000AWS).

Investigational device and comparator The investigational device is Carebot AI MMG (software version 2.9, deep-learning models v2.3), a stand-alone AI system that analyzes 2D FFDM exams and outputs a case-level classification into three categories, together with an internal risk score. Two predefined operating points are evaluated: a high-sensitivity (HSe) threshold, where both benign and malignant masses are treated as "positive" (rule-out setting), and a high-specificity (HSp) threshold, where only malignant masses are counted as "positive" (rule-in setting).

As a human comparator, three experienced radiologists (RAD 1-3) independently read the same anonymized studies using a dedicated DICOM viewer integrated with a labeling application. Radiologists were blinded to AI outputs, clinical information, and outcomes and recorded a case-level classification into the same three categories (Negative/Benign/Malignant). For primary analyses, their binary decisions are derived using the same HSe and HSp rules. In addition, a "random reader" benchmark is constructed for balanced accuracy by repeatedly sampling one radiologist's decision per case in a bootstrap framework (20,000 iterations).

Reference standard The reference standard is established at the study level. A case is labeled "Malignant" if there is histopathological confirmation of breast cancer from biopsy performed in temporal association with the index mammogram. A case is labeled "Non-malignant" if there is consensus between two local radiologists that the finding is negative or stably benign, typically corroborated by at least 2 years of imaging follow-up. Tumor staging (e.g., TNM) is not used in the present analysis. All 48 malignant cases from the participating centers are included in the analytical subset; no cancer-positive examinations were excluded.