Time is of the essence when it comes to diagnosing symptoms and delivering treatment. More than 100 hospitals have turned to AI to detect abnormalities and accelerate time to diagnosis with AI.
These hospitals in Myanmar, New Zealand, the US, and Vietnam are using DrAid, an AI software for automated X-ray diagnostics that is among the first AI platforms to be cleared by the FDA to detect features suggestive of collapsed lungs from chest X-rays.
The creator of DrAid is VinBrain, a health-tech startup in Vietnam that is also part of NVIDIA Inception programme.
Trained on a dataset of more than 2.5 million images, DrAid applies AI analysis to medical images for more than 120,000 patients each month.
VinBrain is also building a host of other AI applications, including a telehealth product that analyses lab test results, medical reports and other electronic health records.
“Multi-modal data is key to delivering precision care that can improve patient outcomes. Our medical imaging models, for instance, can analyse chest X-rays and make automated observations about abnormal findings in a patient’s heart, lungs and bones,” said Steven Truong, CEO of VinBrain.
The VinBrain team has developed more than 300 AI models that process speech, text, video, and images including X-ray, CT and MRI data.
“Healthcare is complex, so the pipeline requires hundreds of models for each step, such as preprocessing, segmentation, object detection and post-processing. We aim to package these models together so everything runs on GPU servers at the hospital — like a refrigerator or household appliance,” said Truong.
VinBrain recently launched DrAid Appliance, an on-premises, NVIDIA GPU-powered device for automatic screening of medical imaging studies that could improve doctors’ productivity by up to 80 percent.
The company also offers a hybrid solution, where images are preprocessed at the edge with DrAid Appliance, then sent to NVIDIA GPUs in the cloud for more demanding computational workloads.
VinBrain trains its AI models using NVIDIA DGX SuperPOD which speeds up training100x faster than when done only with CPU, significantly shortening the turnaround time for model development.
It also uses software from NVIDIA AI Enterprise, an end-to-end solution for production AI, which includes the NVIDIA Clara platform, the MONAI open-source framework for medical imaging development and the NVIDIA NeMo conversational AI toolkit for its transcription model.
NVIDIA GPUs are used to improve run-time efficiency and deployment. NVIDIA Triton inference server and NVIDIA TensorRT streamline inference for more than hundreds of AI models on cloud-based NVIDIA Tensor Core GPUs.
“We shifted to NVIDIA GPUs for inference because of the higher throughput, faster response time and, most importantly, the cost ratio,” said Truong.
“In the coming years, we want to become the top company solving the problem of multimodality in healthcare data. Using AI and edge computing, we aim to improve the quality and accessibility of healthcare, making intelligent insights accessible to patients and doctors across countries,” he added.
Entelechy Asia 