Bridging gap between AI Technology and Clinical practice

Radiologist-supervised SIFT delivers the world’s largest, most comprehensive, pixel-level annotated radiology dataset: 4,192 NLST CTs (11,646 nodules) and 831,283 chest X-rays (1,702,526 abnormalities) spanning 15 lesion classes and 67 subclasses, transforming trusted public datasets into a foundational resource powering AI, MedTech, and biomedical innovation worldwide.
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Medical Image Annotation is a foundational element in the Advancement of AI

High quality medical image annotation contribute significantly to research, diagnosis and patient care.
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Medical Image Annotation and Its Challenges

Medical image annotation is a complex process that involves adding labels, descriptions, or metadata to medical images such as X-rays, MRIs, CT scans. This used to be done manually by humans, so seekers need to hire the professionals having significant knowledge and experience in annotating the images in precise manner. At Caddie Technologies we have transformed this manual process into an automated process.

Key challenges in Medical Image Annotation

Variability

Medical images vary due to differences in equipment and techniques, requiring large datasets that are challenging to gather.

High Costs and Scalability

Reliance on experts like physicians leads to high costs and limited scalability in medical imaging annotation.

Data Privacy and Security

Maintaining a fine balance between giving annotators access and ensuring data security is crucial.

Manual Intensive

The need for expertise and the variability of images make the annotation process very manual-intensive.

Physician Burnout

The intensive manual annotation process puts a significant strain on medical professionals.

Specialize Knowledge

Requires deep medical expertise to accurately identify anatomical structures and abnormalities.

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Caddie's solutions are for?

Al Companies

Al Companies

Several AI companies rely on accurate and expertly annotated medical data to develop robust algorithms. AI companies who want to accelerate research and gain regulatory compliance, Caddie’s medical image annotations are available in leading image data centers and CRO services powered by experts will help reach their goals faster.

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University Researchers

University Researchers

With rising expectations, clinical development faces the need for targeted and meaningful data. Caddie’s AI which can analyze diverse data sources is desired. Researchers often deal with vast amounts of unstructured medical data. Extracting relevant information from this is challenging. Caddie can help discover, curate, segment and modularize imaging data for research.
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Clinical Researchers

Clinical Researchers

Clinical researchers are venturing into uncharted territories using AI and data analytics to unlock new possibilities in healthcare. Caddie’s AI tools not only offer operational efficiency but also provide Scientific AI Powered by generative AI (gen AI) and foundational models can revolutionize trial design and success rates.

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Our Collaborators

Pioneering Medical AI Excellence: Visionary Minds Uniting to Transform Healthcare
Through Advanced Image Analysis and Innovative Artificial Intelligence Solutions

Dr. Kunio Doi

Emeritus Professor, Univ Chicago, Recognized as father of CAD using computerized image analysis and artificial intelligence, that improves diagnoses using clinical images worldwide

Dr. Maryellen Giger

A.N. Pritzker Distinguished Service Professor of Radiology, University of Chicago Member of US National Academy of Engineering (NAE)

Dr. Fleming Lure

Received the first FDA pre-market approved (PMA) early-stage lung cancer detection system on radiograph.

Dr. Stefan Jaeger

He is a staff scientist at the Lister Hill National Center for Biomedical Communications at the United States National Library of Medicine (NLM), which is part of the National Institutes of Health (NIH).

Vassilios Raptopoulos

Professor of Radiology, Harvard Medical School, and Vice Chair for Clinical Services at Radiology Department of Beth Israel Deaconess Medical Center. Former President of the New England Roentgen Ray Society.

Industries We Serve

healthcare

Healthcare

auto

Auto

manufacturing

Manufacturing

technology

Technology

retail

Retail

defence

Defence

Testimonials

High efficiency of SIFT predictions makes it attractive for tuberculosis diagnostics and monitoring. Multi-domain, patient-centric databases from NIAID TB Portals could be used to align SIFT predictions with retrospective data on treatment history, drug sensitivity, history of relapses, influence of comorbidities and treatment outcomes.

NIAID TB Portals

For AI to add the most value for patients and physicians, it needs to support, not supplant

Steven Lin
MD, a clinical assistant professor of medicine at Stanford University

SIFT promises adoption of federated learning by offering a critical component in powering a sustainable, inclusive learning system.

Clinicians

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Accelerate your research and enhance diagnostic accuracy with Caddie's solutions.
Connect with our team to discuss your AI and medical imaging needs

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18310 Montgomery Village Ave
Ste 300 #1238
Gaithersburg, MD 20879
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