See-Mode Technologies Secures FDA Clearance for AI-Powered Thyroid Ultrasound Analysis and Reporting Software

See-Mode Technologies, a leader in AI-driven ultrasound imaging solutions, has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its thyroid ultrasound analysis and reporting software.

See-Mode’s innovative AI technology aims to streamline thyroid ultrasound reporting, reducing both time and variability in clinical care. The software identifies nodules in single or multinodular thyroid images, automatically classifying each one according to the American College of Radiology’s (ACR) TI-RADS rating system. With the software’s assistance, a comprehensive worksheet is generated, and preliminary impressions are seamlessly integrated into radiology reporting systems following clinician review and approval. Additionally, available CPT codes for AI-aided thyroid ultrasound analysis offer enhanced reimbursement opportunities.

This software marks the first FDA-approved product for both detection and diagnosis (CADe/x) in thyroid ultrasound. It stands out for its high degree of automation, automatically detecting and assessing thyroid nodules without manual intervention. Clinicians can quickly review and modify AI-generated results before finalizing reports. It also simplifies follow-up thyroid study reporting, addressing a critical and often time-intensive task for radiologists.

Dr. Sadaf Monajemi, Co-Founder of See-Mode, stated: “The results of a multi-reader multi-case (MRMC) study, that formed part of the FDA submission, showed improvement in radiologist performance with the aid of See-Mode. We observed that See-Mode enhanced the performance of radiologists in nodule localization, characterization, and ACR TI-RADS level agreement, leading to improved differentiation between benign and malignant thyroid nodules.”

Dr. Milad Mohammadzadeh, Co-Founder of See-Mode, added: “We are proud to receive FDA clearance for our thyroid ultrasound solution. By bringing AI into routine clinical practice, we aim to reduce the reporting time and inter-operator variability that exists in thyroid ultrasound.”