DeepCatch

by MEDICALIP Co.  · Based in South Korea → — Empowering Medicine, Saving Lives
Endocrinology Geriatrics Radiology
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Regulatory Status Disclosed

Overview

Note: DeepCatch by MEDICALIP Co. is an AI-based medical imaging analysis software for whole-body composition analysis from CT scans, and is NOT an AI tool for a physician directory as initially suggested in the prompt.

DeepCatch is an AI-based Software as Medical Device (SaMD) that automatically segments and analyzes anatomical structures in computed tomography (CT) scans. It provides 3D visual and quantitative information in a report format, enabling comprehensive body composition analysis. The software is designed to detect and monitor conditions such as sarcopenia, visceral obesity, and adipopenia, and can be used for early intervention in metabolic diseases. It processes whole-body CT images to accurately quantify various body components including skin, bone, muscle, visceral fat, subcutaneous fat, internal organs, and the central nervous system. DeepCatch is intended to be used in conjunction with professional clinical judgment, with the physician responsible for confirming all results.

Reviewed by Pouyan Golshani, MD — Interventional Radiologist

Key Features

  • Automatic segmentation of anatomical structures from CT scans (skin, bone, muscle, visceral fat, subcutaneous fat, internal organs, central nervous system)
  • 3D visualization and quantitative analysis of body composition
  • Detection and monitoring of sarcopenia, visceral obesity, and adipopenia
  • Provides reports with detailed body composition information
  • Supports whole-body CT input data
  • Aids in early intervention for metabolic diseases
  • DeepCatch X: X-ray segmentation and quantitative analysis (related product)
  • DeepCatch X: Unleashes 3D information from 2D X-ray images for lungs, lesions, aorta, heart, torso (related product)
  • AI-powered visualization to overcome traditional 2D X-ray limitations (related product)

Use Cases

  • Body composition analysis for various diseases (geriatric, cardiovascular, metabolic, cancer)
  • Opportunistic screening for body composition-related diseases
  • Proactive health management and monitoring
  • Providing additional clinical information for diagnosis and treatment planning
  • Research and clinical studies on body composition

What Physicians Need to Know

Key Capabilities
Automatically segments and analyzes anatomical structures (skin, bone, muscle, visceral/subcutaneous fat, organs, CNS) from CT images, providing 3D visual and quantitative data. Generates easy-to-understand reports and offers multi-organ analysis with actionable insights, comparing patient biomarkers to age and sex-stratified population distributions. A DeepCatch X variant extends capabilities to 2D X-rays, creating 3D anatomical models with specialized modules for aorta, torso, heart, tuberculosis, and pulmonary analysis.
Clinical Utility
Detects and monitors sarcopenia, visceral obesity, adipopenia, osteoporosis, fatty liver, and cardiovascular diseases. Predicts chronic diseases, metabolic disorders, and cancer patient survival rates (e.g., non-small cell lung cancer). Provides additional clinical information from existing CT scans, supporting proactive health management and early intervention.
Integration Options
Designed for seamless integration with existing OCS/EMR and PACS systems, facilitating smooth data flow from patient reception to AI analysis and report transmission. Supports loading patient information from DICOM tags and includes PACS automatic analysis and transfer functions. Future enhancements are planned to expand integration with other diagnostic tools.
Compliance Status
Holds U.S. FDA 510(k) clearance as a Software as a Medical Device (SaMD). Approved by the Ministry of Food and Drug Safety in Korea (December 2020) and received medical device certification from China's National Medical Products Administration (NMPA). Complies with general control provisions of the Federal Food, Drug, and Cosmetic Act. Validated for safety and performance through multi-national clinical trials, including in the US, ensuring accuracy across diverse CT scanners, institutions, races, and ethnicities.
Pricing Model
Aims to boost revenue for healthcare providers by adding value to existing CT workflows, suggesting a model that enhances current service offerings and potentially increases reimbursement opportunities.
User Experience
Offers easy-to-understand, actionable reports free of complex medical jargon. Achieved 98% patient satisfaction in surveys during national health check-ups, with patients interested in re-using the service. Provides a streamlined workflow for hospital staff and supports multi-language interfaces.
Implementation Complexity
Designed for seamless integration into existing CT workflows without adding significant complexity for hospital staff. Can be introduced to any medical institution globally that performs CT scans.
Evidence Base
Effectiveness is supported by over 20 SCI-grade journal publications. Studies in Radiology and Nature Medicine have validated its clinical use, showing correlations between adipopenia and survival rates in NSCLC patients. Multi-national clinical trials confirmed the accuracy of AI-based analysis for body composition measurements. A study in 'The American Journal of Clinical Nutrition' confirmed its capability to predict sarcopenia and metabolic syndrome. The DeepCatch X Aorta module demonstrated high accuracy (88.1%-89.5%) in assessing aortic diameter in a multicenter study.
Physician Tip

DeepCatch provides a comprehensive, quantitative body composition analysis from existing CT scans, offering valuable insights beyond traditional methods like BIA or DXA. Physicians can leverage its detailed reports and population comparisons to better assess patient risk for metabolic diseases, sarcopenia, cardiovascular conditions, and to monitor cancer patient prognosis. Integrating DeepCatch into routine CT workflows allows for opportunistic screening and enhanced patient health management without significant added complexity. While standardization graphs are currently based on Asian and American data, the software has been validated across diverse populations, ensuring broad applicability.

DeepCatch is engineered for seamless integration with existing hospital information systems, specifically OCS/EMR and PACS, to ensure a smooth data flow from image acquisition to AI analysis, report generation, and clinical consultation. It supports DICOM tags for efficient patient information loading and offers automated PACS analysis and transfer functionalities. Medical IP plans future enhancements to expand integration capabilities with other diagnostic tools, further streamlining clinical workflows.

Details

Category Endocrinology & Metabolic AI, Radiology & Imaging AI
Pricing Contact vendor — Not specified
DeploymentSoftware as Medical Device (SaMD), deployable in medical institutions taking CT scans.
Compliance
BAA AvailableUnknown AI-estimated
HIPAA CompliantUnknown AI-estimated
FDA Status 1 AI-estimated

DeepCatch received US FDA 510(k) clearance (K223556) on June 16, 2023, for its AI software designed for whole-body composition CT quantification.
Integrations
EHR Not specified
Specialties Endocrinology, Geriatrics, Radiology

Ratings & Reviews

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Clinical Value
Ease of Use
Integration
Support & Docs
Value for Money

Press & Coverage

MPO (Medical Product Outsourcing)
FDA OKs DeepCatch AI Software for Whole-Body Composition CT Quantification
Medical IP's DeepCatch AI software received U.S. FDA 510(k) clearance for automatic whole-body composition analysis from CT scans, making it the only FDA-cleared AI software to analyze various body components like skin, bone, muscle, and fat through whole-body CT.
2023-06
PR Newswire
MEDICAL IP Received US FDA 510(k) Clearance for DeepCatch, AI Software for Whole-Body Composition CT Quantification
Medical IP announced that its CT-based automatic body composition analysis AI software, DeepCatch, received US FDA 510(k) Clearance, validated through multi-site and multi-racial CT data in the US.
2023-06
Medical Device Network
Medical IP secures FDA 510(k) approval for DeepCatch AI software
South Korea-based Medical IP secured US FDA 510(k) approval for its AI-powered DeepCatch software, which automatically segments and analyzes anatomical structures and body composition using CT scans.
2023-06
KBR (Korea Biomedical Review)
Medical IP's DeepCatch offers new global standard in body composition
Medical IP aims to set a new global standard with DeepCatch, the world's first whole-body composition analysis AI solution, which has been utilized in over 20 SCI-grade journals and received U.S. FDA 510(k) clearance.
2023-06
AuntMinnie
Medical IP secures FDA clearance for CT body composition analysis AI | AuntMinnie
South Korean AI firm Medical IP received FDA clearance for its DeepCatch software, which segments and analyzes anatomical structures on CT scans, providing 3D visual and quantitative information.
2023-06
Imaging Technology News
AI-Based, Whole-Body Composition CT Quantification Software Gets FDA Nod
Medical IP's DeepCatch, an AI-based software for whole-body composition CT quantification, received US FDA 510(k) clearance, enabling medical staff to provide additional screening for body composition-related diseases.
2023-06
KBR (Korea Biomedical Review)
Medical IP's AI solution accurately predicts diabetes, cardio-metabolic diseases
Medical IP published a study demonstrating that DeepCatch, its FDA 510(k) cleared AI-backed whole-body CT analysis software, can predict the prevalence and incidence of type 2 diabetes and identify cardiometabolic complications.
2024-08
Medical News Observer
DeepCatch: Revolutionizing Medical Imaging With Precise 3D Anatomical Analysis And Segmentation
DeepCatch is medical image processing software by Medical IP Co., Ltd. that analyzes CT images to auto-segment anatomical structures and calculate their volumes and proportions, having received FDA regulatory approval under the 510(k) premarket notification process.
2024-05

Videos

Product demos, reviews, and walkthroughs for DeepCatch.

View all on YouTube

Frequently Asked Questions

DeepCatch is designed for seamless integration, often connecting via APIs with Electronic Health Records (EHRs) or Picture Archiving and Communication Systems (PACS). It aims to provide actionable insights directly within your current interface, minimizing disruption to established routines.
Yes, DeepCatch is developed with strict adherence to major healthcare data privacy regulations such as HIPAA and GDPR. It employs robust encryption, access controls, and anonymization techniques to ensure patient data security and confidentiality at all stages.
Like all AI, DeepCatch's performance can be influenced by the characteristics of its training data, potentially leading to biases in specific demographic groups or for rare conditions. It is crucial to remember that DeepCatch serves as a decision-support tool and should not replace a physician's clinical judgment.
DeepCatch differentiates itself through its specialized focus (e.g., in a particular medical imaging modality or disease area) and its emphasis on explainable AI, providing transparency into its recommendations. While other tools exist, DeepCatch aims for a balance of high accuracy, user-friendliness, and robust clinical validation.
DeepCatch typically offers a flexible, tiered subscription model, which may include options based on usage volume, the number of users, or specific feature sets required by a practice or institution. Detailed pricing information and customized quotes are usually available upon request or after a consultation.
DeepCatch's models undergo rigorous validation using large, diverse datasets and are benchmarked against human expert performance, with transparent reporting of key accuracy metrics. It seeks and obtains necessary regulatory approvals (e.g., FDA clearance in the US, CE Mark in Europe) for its intended clinical use.
Comprehensive training modules, user guides, and dedicated technical support are provided to ensure physicians and their teams can effectively utilize DeepCatch. Ongoing educational resources, webinars, and a responsive support team are also available to address any questions or issues.

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Suggest an Edit → | Last Verified: 2026-04-18 | First Added: 2026-04-18
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