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§ Private Profile · Level 3 / 162 Collins Street Melbourne, Victoria 3000, AU
See-Mode Technologies is a technology company.
See-Mode Technologies develops AI-enhanced software for ultrasound analysis and reporting, primarily focusing on thyroid and breast imaging. Its core product leverages artificial intelligence to automatically detect and classify nodules, such as applying the American College of Radiology’s (ACR) TI-RADS rating system for thyroid scans. This solution aims to streamline clinical workflows by automating report generation, thereby reducing reporting times and minimizing inter-operator variability in diagnostics.
The company was co-founded in 2017 by Dr. Sadaf Monajemi and Dr. Milad Mohammadzadeh. Their insight stemmed from the need to improve diagnostic accuracy and efficiency in ultrasound imaging, addressing the challenges faced by clinicians in routine practice. Both founders, through their technical and medical expertise, envisioned a platform that could augment radiologist performance by providing robust AI-driven analysis.
Clinicians, particularly radiologists and sonographers, utilize See-Mode's technology to enhance their diagnostic capabilities. The company's vision centers on transforming healthcare delivery by seamlessly integrating AI into routine clinical practice, ultimately driving better outcomes for patients globally. See-Mode continually focuses on improving clinical workflows and diagnostic precision for better patient care.
See-Mode Technologies has raised $10.0M across 2 funding rounds.
See-Mode Technologies has raised $10.0M in total across 2 funding rounds.
See-Mode Technologies has raised $10.0M in total across 2 funding rounds.
See-Mode Technologies's investors include MassMutual Ventures Southeast Asia, Amasia, Amity Ventures, Blackbird Ventures Australia, Kevin Ding, Goodwater Capital, Menlo Ventures, NKM Capital, Polychain Capital, Tekton Ventures, Dylan Taylor, Matt Mazzeo.
See-Mode Technologies has raised $10.0M across 2 funding rounds. Most recently, it raised $9.0M Series A in August 2020.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| Aug 1, 2020 | $9M Series A | MassMutual Ventures Southeast Asia | Amasia, Amity Ventures, Blackbird Ventures Australia, Kevin Ding, Goodwater Capital, Menlo Ventures, NKM Capital, Polychain Capital, Tekton Ventures, Dylan Taylor, Matt Mazzeo, Cocoon Capital, Entrepreneur First, Sginnovate | Announced |
| Feb 1, 2019 | $1M Seed | Cocoon Capital | Amasia, Amity Ventures, Blackbird Ventures Australia, Kevin Ding, Goodwater Capital, Menlo Ventures, NKM Capital, Polychain Capital, Tekton Ventures, Dylan Taylor, Matt Mazzeo, Sginnovate | Announced |
See-Mode Technologies is a MedTech startup developing AI-powered software for automated analysis and reporting of thyroid and breast ultrasound images, primarily serving radiologists, sonographers, and clinicians to enhance diagnostic accuracy, reduce reporting time, and improve workflow efficiency.[1][2][3][4] The company addresses key challenges in medical imaging, such as inter-operator variability, errors in nodule/lesion detection, and time-intensive reporting, by using deep learning to generate standardized reports based on TI-RADS (thyroid) and BI-RADS (breast) criteria.[1][3][4] Initially focused on stroke prediction via computational modeling, it pivoted to ultrasound reporting solutions with strong growth momentum, evidenced by regulatory approvals across the US (FDA-cleared for thyroid), Canada, Australia, New Zealand, and Singapore, plus a June 2025 acquisition by RadNet's DeepHealth division, which integrates its tech into broader population health platforms and reports up to 30% scan time reductions in real-world deployments.[2][3][4]
See-Mode Technologies was founded by Sadaf Monajemi, PhD (Co-founder & Director) and Milad Mohammadzadeh (Co-founder & Director), a team blending scientists, engineers, clinicians, and machine learning experts based in Singapore with operations in Australia.[1][2][5] The idea emerged from applying cutting-edge deep learning and computational modeling to medical images, initially targeting stroke prediction—a leading global cause of death—to help doctors optimize treatments.[1] Early traction built through regulatory milestones, including 2023 approvals in Australia and New Zealand for breast/thyroid ultrasound tools, followed by FDA clearance for its SMART-T (See-Mode Augmented Reporting Tool, Thyroid) in 2024, and partnerships with institutions like RadNet, Duke University radiologists, and I-MED Radiology.[2][3][4][5] Backed by APAC VCs such as MassMutual Ventures, Blackbird Ventures, Cocoon Capital, and SGInnovate, the company scaled to commercial deployment before its 2025 acquisition by RadNet, marking a pivotal validation of its technology.[1][2]
See-Mode rides the AI-in-medical-imaging wave, where deep learning addresses radiologist shortages, rising imaging volumes, and precision demands amid chronic disease burdens like thyroid cancer and breast lesions.[1][2][4] Timing aligns with post-2023 regulatory accelerations (FDA, ANZ, etc.) and 2025 consolidations like RadNet's acquisition, fueled by market forces such as aging populations, ultrasound's cost-effectiveness over MRI/CT, and AI's proven ROI in workflows.[2][3] It influences the ecosystem by standardizing reporting (TI-RADS/BI-RADS automation), enabling population health insights via DeepHealth integration, and partnering with leaders like Monash Health and Stanford, thus lowering barriers for global adoption in underserved regions like APAC.[1][2][5]
Post-acquisition, See-Mode's tech will expand within RadNet/DeepHealth as a core ultrasound engine, driving new AI solutions for diverse anatomies and fueling revenue growth in outpatient imaging.[2] Trends like multimodal AI (combining ultrasound with other modalities) and regulatory harmonization will amplify its reach, evolving its influence from standalone innovator to embedded platform leader in value-based care. As AI reshapes diagnostics, See-Mode exemplifies how targeted MedTech unlocks imaging's hidden insights, delivering the efficient, accurate tools clinicians need to save lives from stroke risks to cancer detection.[1][2][4]