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Organization Contact Information | |
| Name: | RadiSen Co. Ltd. |
| Street 1: | 401, 46, Hoam-ro 26ga-gil, Gwanak-gu |
| Street 2: | |
| City: | Seoul |
| Province: | |
| Post Code: | |
| Country: | Republic of Korea |
| Phone: | +82-70-7525-2147 |
| Organization Email: | contact@radisentech.com |
| Web Site: | http://www.radisentech.com/ |
| Other Online Presence: | |
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General Information | |
| Board Constituency: | None |
| Is your organization legally registered in your country: | Yes |
| If yes, please enter your registration number: | 7928800581 |
| Organization Type - Primary: | Private Sector |
| Organization Type - Secondary: | Company |
| Organization Description: | |
| 1. Mission and Main Focus of RadiSen RadiSen is committed to revolutionizing global healthcare through innovative Digital Radiography Solutions. Our mission is to eliminate Tuberculosis (TB) and enhance lung health by delivering portable X-ray systems and AI-powered screening and diagnostic software that save lives and improve outcomes. 2. Why RadiSen is Focused on Tuberculosis Leveraging advanced digital radiography and AI technology, RadiSen addresses TB, a leading cause of death among people with HIV and a major driver of antimicrobial resistance. Our goal is to bring AI screening solutions to underserved markets, enabling early detection, precise diagnoses, and better patient outcomes. 3. RadiSen’s Contribution to TB Control RadiSen’s AI solutions detect TB, pneumonia, and other chest abnormalities with 95% accuracy in just 10 seconds. Seamlessly integrating with existing X-ray machines, our technology ensures ease of adoption for healthcare providers. With over 70 successful global deployments—including a collaboration in the Philippines that screened 20,000 participants in just three months—we actively drive TB control initiatives worldwide. Moving forward, we aim to expand our impact through partnerships that enhance TB screening and control across diverse regions. |
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| Do you know about the UNHLM declaration: | Yes |
Specializations / Areas of Work | |
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Delivery of health services and care Research and Development |
Other Organization Information | |
| Total number of staff in your organization: | 26 - 50 |
| Number of full-time staff who are directly involved with TB: | 6 - 10 |
| Number of part-time staff who are directly involved with TB: | 0 |
| Number of volunteers who are directly involved with TB: | 0 |
| How did you hear about the Stop TB Partnership: | Attendance at a TB related event |
| If you were informed or referred by another partner of the Stop TB Partnership please tell us who: | |
| Why do you wish join the Stop TB Partnership: | Network with other partners |
| Are you a member of a Stop TB national partnership: | Republic of Korea |
| Are you in contact with your national TB programme: | Yes |
| Please tell us how your organization is contributing to your country's national TB control plan: | |
| We participated in the Union World Conference in 2023 and 2024, as well as the Asia Pacific Region Conference of the International Union Against Tuberculosis and Lung Disease (APRC 2024). During these events, we engaged with officers and doctors from the Asia-Pacific region. Currently, we are in discussions about active TB case-finding projects in Southeast Asian countries, including the Philippines and Vietnam. |
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Geographical Reach | |
| Which country is your headquarters located in: | Republic of Korea |
| Which countries do you do operate in: (This includes countries you are conducting activities in) |
Ethiopia India Indonesia Philippines Thailand Viet Nam |
Contribution | |
| Please tell us how your organization will contribute to the Global Plan to Stop TB by briefly describing its involvement in any of the areas of work listed below: | |
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New Diagnostics: RadiSen's AI capabilities swiftly detect TB, Pneumonia, and chest abnormalities with approximately 95% accuracy in 10 seconds. Our seamless solutions integrate with existing X-ray machines, ensuring easy adoption for healthcare providers. With over 60 successful global deployments, mostly in Southeast Asia, including a recent collaboration in the Philippines that screened 20,000 participants in three months, we actively contribute to TB control initiatives worldwide. Research: Artificial Intelligence Assisted Pulmonary Tuberculosis Detection from Chest Radiographs: A Facility Based Cross-Sectional Study at a TB Specialized Hospital in Ethiopia Published Union Conference on Lung Health (2024) Authors Metasebia Mesfin, M.D.1, Abraham Eshetu, M.D.1, Robel Gemechu1, Abel Worku1, Desalegn Abebaw, Ph.D.2, Tariku Mengesha, M.D.1 Affiliations 1Kidus Petros Hospital, Addis Ababa, Ethiopia. 2Artificial Intelligence Engineering Division, RadiSen Co. Ltd., Seoul, Korea Summary This study evaluates the detection performance of pulmonary Tuberculosis (TB) by a commercially available Artificial Intelligence (AI) software (AXIR-CX) from chest radiographs in Kidus Petros, a TB treatment hospital in Ethiopia. Prediction performances of the AI and expert radiologists are evaluated with the ground truth of Xpert MTB test results. Background AI is increasingly embraced for detecting TB and related abnormalities worldwide. This study retrospectively evaluates the performance of AXIR-CX (version 2.5.0), an AI software developed by RadiSen in South Korea, for detecting TB using radiographs from Kidus Petros hospital. Methods We collected Xpert results and chest radiographs of 1,579 (with 10% positives) individuals seen at the hospital in 2023. The AI’s predictions were evaluated on the entire dataset. Subsequently, a subset of 321 radiographs (49% positives) were interpreted by three radiologists, two from South Korea and one from the hospital, with 10+ years of experience. The radiologists rated the radiographs for TB presence on a scale of 0 to 5, while the AI software provided probability values (0-100%). We compared predictive performances of the hospital’s radiologist(without and with the help of AI), the joint South Korean radiologists, and the AI against Xpert results. Results In the entire dataset, the AI predicted with sensitivity, specificity, and accuracy of 71%, 83%, and 82% respectively with an AUC of 0.83. In the sampled dataset of 321 cases, the joint radiologists (JointRads) demonstrated sensitivity, specificity, and accuracy of 54%, 95%, and 75% respectively while the AI achieved specificity and accuracy of 92% and 73% at a similar sensitivity level. The hospital’s radiologist (Rad3) achieved sensitivity, specificity, and accuracy of 83%, 65%, and 74% respectively. At the same sensitivity level, the AI exhibited specificity and accuracy of 67% and 75%. When the radiologist was assisted by AI (Rad3&AI), a slight improvement was observed, with sensitivity, specificity, and accuracy reaching 85%, 68%, and 76% respectively. Conclusion The AI demonstrated a capacity to identify tuberculosis with similar accuracy to skilled radiologists. Collaborative work between a radiologist and AI yields enhanced predictive performance. These findings indicate the potential usefulness of AI in hospital triage scenarios. |
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Declaration | |
| Declaration of interests: There are no known conflict of interest. | |
| Application date: | December 15, 2023 |
| Last updated: | November 26, 2024 |