Diagnostic Accuracy of Carebot AI MMG in Mammography Screening: Multicenter MRMC Study

NCT07298252 | Completed

• 1 other identifier: CB-MMG-02-MC
• Study Type: observational
• Target: 222
• Locations: 2 countries
• Sites: 4

Brief Summary

This study evaluates the diagnostic performance of Carebot AI MMG, an artificial intelligence (AI)-enabled medical device for evaluating mammograms. The software analyzes standard full-field digital mammography (FFDM) images and classifies each examination as having no suspicious finding ("Low Risk"), a probably benign mass ("Medium Risk"), or a suspicious malignant mass ("High Risk"). The study is retrospective and observational. It uses anonymized mammography examinations from four screening centers, without any additional imaging or contact with patients. Three experienced breast radiologists independently read the same set of cases, and their assessments are used as the human benchmark. A histopathology-based reference standard, supplemented by radiologist consensus and follow-up information for negative cases, is used to determine whether cancer is present. The main goal is to compare the AI system with human radiologists in terms of sensitivity and specificity for detecting breast cancer, and to assess whether the AI can achieve non-inferior performance at two predefined operating points: one favoring higher sensitivity and negative predictive value (rule-out) and one favoring higher specificity and positive predictive value (rule-in).

Conditions

Breast Neoplasms; Breast Cancer Detection; Breast Cancer - Female; Breast Cancer Screening

Keywords

Breast cancer screening; Mammography; Full-field digital mammography; Artificial intelligence; Deep learning; Diagnostic accuracy; Carebot AI MMG

Outcome Measures

Primary Outcomes (2)

• Balanced accuracy (BA) of Carebot AI MMG for detecting malignant versus non-malignant examinations

  Balanced accuracy (BA) is defined as the average of sensitivity and specificity for classifying each mammography examination as malignant or non-malignant. BA will be calculated at two pre-specified operating points of the AI system: a high-sensitivity (HSe) setting and a high-specificity (HSp) setting. Performance will be estimated with 95% confidence intervals and compared to a multi-reader benchmark constructed from three experienced radiologists and a bootstrap-based "random reader" reference.

  Baseline (index mammography examination; examinations acquired between 01-01-2025 and 14-11-2025; retrospective assessment

• Sensitivity (Se) of Carebot AI MMG versus histopathology-based reference standard

  Sensitivity is defined as the proportion of malignant examinations correctly classified as positive by the AI system. Sensitivity will be calculated at both the HSe and HSp operating points and compared pairwise with the sensitivity of each of the three radiologists using the same case-level ground truth.

  Baseline (index mammography examination; examinations acquired between 01-01-2025 and 14-11-2025; retrospective assessment

Secondary Outcomes (3)

• Specificity (Sp) of Carebot AI MMG versus histopathology-based reference standard

  Baseline (index mammography examination; examinations acquired between 01-01-2025 and 14-11-2025; retrospective assessment

• Positive predictive value (PPV) of Carebot AI MMG

  Baseline (index mammography examination; examinations acquired between 01-01-2025 and 14-11-2025; retrospective assessment

• Negative predictive value (NPV) of Carebot AI MMG

  Baseline (index mammography examination; examinations acquired between 01-01-2025 and 14-11-2025; retrospective assessment

Study Arms (2)

Malignant cases

Women with biopsy-proven breast cancer included in the analytical subset (n = 48). Each case corresponds to a screening full-field digital mammography (FFDM) examination with all four standard views (LCC, RCC, LMLO, RMLO), retrospectively identified from participating screening centers.

Device: Carebot AI MMG software analysis

Non-malignant cases

Women without histopathological evidence of breast cancer, classified as negative or stably benign by two independent local radiologists with at least 2 years of imaging follow-up (n = 174). Each case corresponds to a screening FFDM examination with all four standard views (LCC, RCC, LMLO, RMLO), retrospectively selected from the same screening population.

Device: Carebot AI MMG software analysis

Interventions

Carebot AI MMG software analysis DEVICE

Retrospective stand-alone AI analysis of anonymized 2D full-field digital mammography (FFDM) examinations. The AI system (Carebot AI MMG, version 2.9) processes existing images and outputs case-level risk classifications; no additional imaging, randomization, or changes to patient management occur as part of this study.

Malignant cases; Non-malignant cases

Eligibility Criteria

• Age: 18 Years+
• Sex: female
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Women undergoing routine screening mammography at four participating centers in Central Europe. The analytical dataset consists of a retrospectively assembled case-control subset of 222 anonymized 2D FFDM examinations (48 malignant and 174 non-malignant) selected from a source cohort of 4,729 screening studies.

You may qualify if:

• Female sex
• Age ≥ 18 years at the time of the screening mammogram
• Screening full-field digital mammography (FFDM) examination with all four standard views (LCC, RCC, LMLO, RMLO) available
• Sufficient image quality and complete DICOM metadata to allow retrospective analysis

You may not qualify if:

• Male sex
• Age \< 18 years
• Digital breast tomosynthesis (DBT/3D) examinations without a corresponding full 2D FFDM four-view series
• Incomplete mammography series (missing one or more of LCC, RCC, LMLO, RMLO)
• Corrupted or unreadable DICOM files
• Missing or inconsistent key metadata (e.g., laterality, view, acquisition date)

Sponsors & Collaborators

• Carebot s.r.o.: lead

Study Sites (4)

Poliklinika MEDICON Budějovická

Prague, 14000, Czechia

Location

Dolnooravská nemocnica s poliklinikou MUDr. L. N. Jégého

Dolný Kubín, 026 14, Slovakia

Location

Nemocnica s poliklinikou Považská Bystrica

Považská Bystrica, 017 01, Slovakia

Location

Ľubovnianska nemocnica

Stará Ľubovňa, 064 01, Slovakia

Location

MeSH Terms

Conditions

Breast Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms by Site; Neoplasms; Breast Diseases; Skin Diseases; Skin and Connective Tissue Diseases

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 14, 2025
• First Posted: December 23, 2025
• Study Start: January 1, 2025
• Primary Completion: November 3, 2025
• Study Completion: November 3, 2025
• Last Updated: January 14, 2026

Record last verified: 2026-01

Data Sharing

• IPD Sharing: Will not share

Locations

SL (3); CZ (1)