Prospective Ultrasound Data Collection Study for AI-Based Detection of Breast Biopsy Markers

Study Design and Setting

This is a prospective, non-interventional observational study designed to collect ultrasound imaging data and related metadata to support the development, refinement, and performance characterization of an artificial intelligence (AI)-enhanced ultrasound software intended to detect and localize standard-of-care breast biopsy markers.

All ultrasound examinations are performed as part of routine clinical care for patients undergoing evaluation and planned surgical excision of a breast tumor. Study participation does not alter standard imaging protocols, clinical workflow, or treatment decisions.

Study Procedures

During a scheduled clinical visit, participants undergo limited B-mode breast ultrasound imaging using an FDA-cleared ultrasound system provided for the study. The system includes investigational software that displays marker localization output during image acquisition. The software output is not used to guide or alter clinical decision-making.

Ultrasound image data are automatically recorded by the system during the examination. Imaging datasets include visualization of the biopsy marker and surrounding breast tissue. When applicable, imaging may occur at different time points along the surgical treatment pathway.

A brief user-experience questionnaire may be completed by the operating physician following image acquisition. De-identified clinical and imaging metadata are recorded in a structured case report form (CRF).

Data Collection and Management

Ultrasound image data are de-identified prior to transfer. De-identified datasets are securely transmitted to the sponsor's controlled research environment for algorithm development and analysis.

Collected variables may include imaging parameters, marker type, breast tissue characteristics, tumor characteristics, and operator experience level. A structured data dictionary defines all variables collected, including source, format, allowable ranges, and coding conventions where applicable.

All study data are stored in compliance with applicable data protection and privacy regulations. Access to study data is restricted to authorized personnel.

Quality Assurance and Monitoring

Data quality procedures include predefined validation checks for completeness, internal consistency, and logical range limits within the electronic data capture system. Automated and manual data review processes are implemented to identify missing, inconsistent, or out-of-range values.

Source data verification may be performed on a risk-based sample of records to confirm consistency between recorded study data and source documents, such as medical records or ultrasound system exports.

Standard Operating Procedures (SOPs) govern study conduct, including patient recruitment, data acquisition, data transfer, data management, adverse event documentation, and change management. Monitoring activities may be conducted according to a risk-based plan.

Statistical Considerations

This study is primarily descriptive and developmental in nature. Analyses will focus on characterization of software marker localization performance, including measures such as detection success, time to localization, and performance across relevant subgroups (e.g., tissue characteristics, operator experience levels).

Agreement analyses may be performed comparing software output to trained reader assessments of recorded ultrasound images. Statistical methods may include descriptive statistics, confidence interval estimation, and exploratory subgroup analyses. The study is not designed as a confirmatory clinical effectiveness trial.

Sample Size Rationale

The planned enrollment is based on feasibility considerations and the need to collect a sufficient number of imaging datasets to support algorithm development and performance characterization across a range of clinical and anatomical conditions. The study is not powered for confirmatory hypothesis testing.