New AI imaging tool to accelerate critical patient diagnoses

Two major IT players have teamed up deploy deep-learning AI to cut the time between medical imaging, diagnosis and beginning treatment.
By Hafizah Osman
10:06 PM

The project, a joint collaboration between Intel and GE Health, is promising to offer physicians automated diagnostic alerts for some conditions within seconds of medical imaging being completed.

It leverages the Intel Distribution of OpenVINO toolkit, running on Intel processor-based X-ray systems to help prioritise and streamline patient care.

Using this system, X-ray technologists, critical care teams and radiologists will be immediately notified to review critical findings that may accelerate patient diagnosis.

Intel Internet of Things Group Health and Life Sciences Sector General Manager David Ryan explained that the AI imaging models are optimised for inference and deployment using the model optimiser component of OpenVINO.

The optimised models are then integrated into the GE application with the OpenVINO inference engine APIs. As X-ray images are acquired by the machine, the inference engine runs them for clinical diagnosis.

GE Healthcare Senior Vice-President of Edison Portfolio Strategy Keith Bigelow said medical imaging is the largest and fastest-growing data source in the healthcare industry.

But, even though it accounts for 90 per cent of all healthcare data, more than 97 per cent of it goes unanalysed or unused.

“Before now, processing this massive volume of medical imaging data could lead to longer turnaround times from image acquisition to diagnosis to care. Meanwhile, patients’ health could decline while they wait for diagnosis,” he said.

“Especially when it comes to critical conditions, rapid analysis and escalation is essential to accelerate treatment.”

According to Bigelow, a key implementation of this technology is providing earlier detection of a potentially life-threatening event – a collapsed lung, also known as pneumothorax.

He said radiologists can now deploy optimised predictive algorithms that scan for and detect pneumothorax “within seconds at the point of care”, allowing rapid response and reprioritisation of an X-ray for clinical diagnosis.

“Deploying deep learning solutions on existing infrastructure delivers the potential to power more efficient and effective care, enhance decision-making, and drive greater value for patients and providers,” he said.

"For the more than 12,000 Australians diagnosed with lung cancer each year, this means a higher chance of survival.”

Ryan said deep learning was a promising approach for radiology because its models can be trained to recognise desired features in an image, such as tumors or anatomies.
 
“Furthermore, training is done by giving numerous labeled example images to the models, without having to specify the exact features to look for. Deep learning can identify details that can be missed by the human eye,” he said.

According to Ryan, in future applications, deep learning models can be used to identify incidental findings, as well as help radiologists manage their workload, enhance quality of scans, and reduce ‘retakes’, which can cause unnecessary exposure to radiation.

“Deep learning is also showing promising results in image reconstruction from the imaging modalities. Future applications of deep learning can extend beyond imaging data to include electronic health records, pathology, cellular microscopy data, etc. to help develop targeted drugs and achieve precision in medicine,” Ryan added. 

Ryan also said deep learning was a promising approach for radiology because its models can be trained to recognise desired features in an image, such as tumours or anatomies.

“Furthermore, training is done by giving numerous labeled example images to the models, without having to specify the exact features to look for. Deep learning can identify details that can be missed by the human eye,” he said.

According to Ryan, in future applications, deep learning models can be used to identify incidental findings, as well as help radiologists manage their workload, enhance quality of scans, and reduce ‘retakes’, which can cause unnecessary exposure to radiation.

“Deep learning is also showing promising results in image reconstruction from the imaging modalities. Future applications of deep learning can extend beyond imaging data to include electronic health records, pathology, cellular microscopy data, etc. to help develop targeted drugs and achieve precision in medicine,” Ryan said. 

This article first appeared on Healthcare IT News Australia.