Outsource Annotation Services

Caddie’s AI portfolio helps researchers and academicians get their research data staged in a perfect manner.

Caddie’s tools help radiologists be more productive, enable faster collaboration across different teams and domains and most importantly dramatically reduces human fatigue errors.

Understanding the Key Obstacles and Caddie’s Solutions

Annotating medical images is challenging for radiologists due to the lack of collaborative tools, slow annotation processes, and the risk of human fatigue. Caddie’s solutions address these issues by providing advanced AI-enabled tools to enhance productivity and reduce errors.

Challenges in Annotating Services

Generally, in this environment radiologists don’t have tools which can lead to a collaborative effort.
Annotation then becomes a slow process which often leads to longer clinical trials and delays product to market.
Radiologists often perform these duties along with other urgent tasks and this introduces human fatigue. This leads to natural human errors.

Caddie Addresses All These Concerns by Offering

Tools which enable efficient radiological workflow enabled with the latest AI technologies can enable radiologists to collaborate.
Productivity can be improved almost 10x times.
Automated and semi-automated tools developed by Caddie Technologies enable radiologists to reduce human errors.

Caddie Technologies boasts a diverse medical and non-medical team spread across various geographical locations. Each team member contributes unique skills to create the perfect blend of expertise. Our team is adept at annotating X-ray, CT, and MRI images, providing a comprehensive approach to data annotation.

Fast and efficient

Automatically annotate a batch of images in a fraction of the time. It has capability to simultaneously label and annotate 66 CXR lesions and 44 CT lesions.

Detailed

It generates fine-grained boundaries and precise locations of lesions.

IP Assets

Effective management of these assets is essential for successful corporate collaboration

High Accuracy

There are nine publications proving SIFT’s^™ ninety-three percent accuracy, robustness, description on images, and on texts.

Collaboration

SIFT^™ is capable of annotating comorbidity and seamlessly integrating with the radiologist workflow.

Unique Capability

Multi-modality annotation information includes confidence scores and semantic text for lesion manifestation.

Areas of Expertise and Contributions

Harnessing Experience, Training, Research, and Collaboration.

Expertise and Experience

Senior radiologists bring extensive knowledge crucial for developing and validating AI models.

Mentorship and Training

Our team focuses on training researchers, enhancing their understanding of nuances across different domains.

Research and Development

Our team aids AI companies by providing data for research, leading projects, and publishing findings to secure funding.

Interdisciplinary Collaboration

Our tools facilitate collaboration between radiologists, data scientists & AI developers, ultimately enhancing patient care.

Quality Control and Validation

Senior radiologists play a critical role in quality control and validation.

Implementation and Integration

Our team oversees the implementation and integration of AI tools into existing workflows.

Diagnostic Accuracy

AI tools augment diagnostic accuracy, but our team first curates data for research, ensuring robust solutions.

Continuous Improvement

We continuously refine AI models through feedback and development, keeping them cutting-edge and clinically relevant.

Industries We Serve

Healthcare

Auto

Manufacturing

Technology

Retail

Defence

Diverse Annotation Experience

Extensive Experience Across Various Imaging Modalities. Our team has a wealth of experience in annotating a wide range of images.

Digital x-ray images: 57 ,706 studies

ChexPert from Stanford U: 263,621 13 different diseases
Chest X-ray14 from Clinical Center of NIH: 141,537 12 different diseases
MIDRC/NIBIB/NIH from 30+ hospitals: 160,020 COVD-1
TB Portals NIAID/NIH: 14,531 TB Cases

Pathological Images: 46,051 studies

Cytopathology e.g., cancer, disease : 33,100
Histopathology e.g., tissue : 12,300

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

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