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The brain tumor detection assistance system resulting from these NSTC research projects, known as the DeepBT Detector, has been exclusively licensed to NYCU and Taipei Veterans General Hospital. The DeepBT Detector has received multiple professional awards, including the "2022 Future Tech Award," the "19th National Innovation Award - Academic Research and Innovation Award," and the "National Innovation Advancement Award."
"AItewan" DeepBT Detector has been approved for Class II medical device registration by the Taiwan Food and Drug Administration (TFDA).
DeepBT is a software system intended to assist trained medical professionals during their clinical workflows of radiation therapy treatment planning. DeepBT is applicable only to adult patients with known imaging diagnosis of acoustic neuroma, meningioma, and brain metastasis. The product can take either single parametric magnetic resonance (MR) images (T1W+C) or bi-parametric MR images (T1W+C and T2W) as inputs, then automatically interpret MR images, and provide the lesion contours without requiring manual preprocessing or annotation.
• Automatic classification of various brain tumors in MRI • Automatic segmentation of various brain tumors in MRI • Automatic detection and segmentation of brain arteriovenous malformations in MRI • Estimation of hemodynamic parameters in MRI • Automatic segmentation of coronary artery calcification in CT scans • Analysis and identification of physiological signals • OthersMORE
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Development of AI Software for Medical Imaging
Using our proprietary AI technology, we perform automatic segmentation of various types of brain tumors (such as acoustic neuroma, meningioma, and brain metastasis) on preoperative MRI scans, providing objective and accurate volumetric analysis of the tumors.
DeepBT™ not only allows viewing DICOM images and inspecting AI results but also offers exceptional scalability to optimize existing workflows for physicians and create additional value. This includes value-added applications such as providing screening prioritization, proactive critical notifications, mobile and remote services, and customized services. These features not only offer more value-added services to medical institutions but also open up broader development opportunities for the application of deep learning technology, further integrating it closely with clinical workflows.
