CAIDE Systems: Opening up Diverse Possibilities in Diagnosis with AI
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CAIDE Systems: Opening up Diverse Possibilities in Diagnosis with AI

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Jacob Lee, Founder & CEO, CAIDE SystemsJacob Lee, Founder & CEO, CAIDE Systems
With current medical imaging diagnosis being prone to delays, human errors, and challenges in medical interpretation, CAIDE Systems has created an artificial intelligence (AI) diagnosis platform. Built on cutting-edge deep learning technology, this top-notch AI medical platform analyses images, (ultrasound, MRI, CT, and X-ray) genome data, and electronic medical records. “For us, developing meaningful products and providing equal access to medical services, ensuring the right to life regardless of one’s social and financial status is very important,” mentions Jacob Lee, Founder and CEO, CAIDE Systems.

The firm’s core strategies are built around two distinctive product roadmaps—a medical diagnosis platform and a disease-specific application product. “Our packaged medical platform—m:Studio—one for ‘research’ and the other for ‘individual’ is the heart of this strategy,” says Lee. m:Studio provides a user-friendly interface for labeling and an intuitive model training environment that needs the least supervision, thereby, ensuring faster creation of models and accurate inference. Moreover, m:Studio Research—a deep learning-based medical research platform—is designed to support the detection of diseases and symptoms in the human body. m:Studio Research focuses on getting large amounts of labeled data, model tuning, and training. Automatic annotation techniques will be an essential tool for supporting listed activities. The firm is working with a Medical Research Center in Chicago—particularly with orthopedic surgeons—that are developing solutions for patients in certain areas of the body.

“We are working with specific themes and applications with orthopedic surgeons for measurement detection and segmentation of abnormal areas, reducing human error and enhancing the workflow in the process,” says Lee. On the other hand, m:Studio Individual—a cognitive artificial disease detection online platform—helps individual patients to freely review their medical images with a second set of eyes or tools, AI. It provides an online diagnosis system with DICOM image files wherein results of specific diseases are assured within a minute via web browsers.

Though doctors are not very open to using this ‘AI detection system’ currently, as they are unsure of the results it could bring forth, CAIDE Systems is going on to build an entire body detection system. With the buzz that AI has been creating in the recent past, the firm believes that it will be utilized in the near future. In fact, CAIDE Systems is connecting with large hospitals in South Korea to test their systems. However, the firm’s brain disease detection system—a part of m:Studio that already contains numerous trained AI model sets— to review CT images and accurately sort hemorrhages and its subtypes, is out in the market and has already proven to be a success. The AI model is trained through 6,000 patients’ data and 140,000 stroke CT images, giving it the capability to detect and state stroke types and locations accurately. It eliminates all human errors and provides the inference result within a minute.

“We have successfully built our functional tools for both disease detections and effective training, and that is our biggest achievement in technology. Now we are actively communicating with customers to utilize this device,” says Lee. The firm is approaching medical research teams and contacting equipment manufacturing companies to find solutions for their hardware devices— turning them into automatic annotation systems. “As of now we are developing our software and are actively looking to provide excellent quality medical services to all in the near future,” adds Lee.

CAIDE Systems is also planning on selling their AI engine to hardware companies and PACS systems such as GE and Philips. Besides, the firm also intends on enhancing its disease detection system and training its AI models to detect over 500 diseases by 2020. “Our goal is to provide a user-friendly platform for consistent, accurate, and fast diagnosis,” concludes Lee.