Providing automated TI-RADS classifications and worksheets, the new AI-enabled software may facilitate improved efficiency with thyroid ultrasound exams.
The Food and Drug Administration (FDA) has granted 510(k) clearance for new AI-powered software geared toward thyroid ultrasound exams.
See-Mode Technologies, the developer of the software, said the software may help standardize assessment of thyroid ultrasound exams through automated TI-RADS classification of thyroid nodules. The company noted that results from a multi-reader, multi-case study demonstrated enhanced localization and characterization of thyroid nodules with adjunctive use of the AI software.
The newly FDA-cleared AI software from See-Mode Technologies for thyroid ultrasound exams reportedly provides automated TI-RADS classification of thyroid nodules. (Image courtesy of Adobe Stock.)
Facilitating workflow efficiency, the software also generates automated worksheets and reportedly streamlines reporting on follow-up thyroid studies, according to See-Mode Technologies.
“By bringing AI into routine clinical practice, we aim to reduce the reporting time and inter-operator variability that exists in thyroid ultrasound,” noted Milad Mohammadzadeh, a co-founder of See-Mode Technologies.
The company added that existing CPT codes for adjunctive AI interpretation of thyroid ultrasound may be applied for use of the See-Mode Technologies software.
The Reading Room: Artificial Intelligence: What RSNA 2020 Offered, and What 2021 Could Bring
December 5th 2020Nina Kottler, M.D., chief medical officer of AI at Radiology Partners, discusses, during RSNA 2020, what new developments the annual meeting provided about these technologies, sessions to access, and what to expect in the coming year.
Can Ultrasound-Based Radiomics Enhance Differentiation of HER2 Breast Cancer?
March 11th 2025Multicenter research revealed that a combined model of clinical factors and ultrasound-based radiomics exhibited greater than a 23 percent higher per patient-level accuracy rate for identifying HER2 breast cancer than a clinical model.