Could Deep Learning Offer Quicker Acute Stroke Detection on Brain MRI Without the Need for T2WI Sequences?

But what are you using this MRI for if you've already done this fast stroke diagnosis?

TIME IS BRAIN and MRIs take a long time. Depending on the size of the area being scanned and how many images are taken, the whole procedure will take 15 to 90 minutes. 1.9 million neurons die per minute, so why are you letting that many die?

Hats off to Helmet of Hope - stroke diagnosis in 30 seconds; February 2017 

Smart Brain-Wave Cap Recognises Stroke Before the Patient Reaches the Hospital

 October 2023

And then this to rule out a bleeder.

New Device Quickly Assesses Brain Bleeding in Head Injuries - 5-10 minutes April 2017

The latest here:

Could Deep Learning Offer Quicker Acute Stroke Detection on Brain MRI Without the Need for T2WI Sequences?

Noting an average processing time of 24 seconds for deep learning detection of acute ischemic stroke on brain MRI, the authors of a new study said deep learning assessment of DWI and FLAIR sequences had equivalent sensitivity and AUROC to T2WI MRI.

Emerging research involving over 900 patients suggests the use of deep learning on brain magnetic resonance imaging (MRI) scans may obviate the need for T2WI MRI sequences in diagnosing acute ischemic stroke.

For the retrospective study, recently published in Academic Radiology, researchers evaluated the capability of a deep learning research application (Neuro Triage Application, version 1.2.5, Siemens Healthineers) in detecting acute ischemic stroke (AIS) in a cohort of 947 people (mean sage of 64) who had diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery sequences with brain MRI.

At an average processing time of 24 seconds, the deep learning application offered a 90 percent sensitivity rate, and 89 percent specificity rate and a 95 percent area under the receiver operating characteristic curve (AUROC) for diagnosing AIS on brain MRI, according to the study authors.

In a new study, researchers found that a deep learning application for brain MRI offered a 90 percent sensitivity rate and a 95 percent area AUROC for diagnosing acute ischemic stroke at an average processing time of 24 seconds. (AI-generated image courtesy of Adobe Stock.)

In a subgroup of 71 people who had additional T2WI sequences, the researchers found equivalent sensitivity (88 percent) and AUROC rates (88 percent) between the deep learning application (with no T2WI sequences) and the use of T2WI sequences. They also noted comparable specificity (81 percent for deep learning vs. 84 percent for T2WI) and accuracy rates (85 percent for deep learning vs. 83 percent for T2WI).

However, the study authors pointed out that use of T2WI sequences resulted in a 48-second processing time in comparison to 34 seconds with the deep learning application.

“ … The addition of T2WI to DWI and FLAIR does not provide any advantage when (the deep learning application) is employed to detect AIS. Given that the golden hour for stroke intervention is within 180 to 270 min, our findings suggest that T2WI can be omitted to shorten the detection time for AIS because any potential time delays caused by T2WI cannot be overlooked or regarded as minor,” wrote lead study author Jimin Kim, M.D., who is affiliated with the Department of Radiology at Eunpyeong St. Mary’s Hospital and the Catholic University of Korea College of Medicine in Seoul, Korea, and colleagues.

Assessing the performance of the deep learning application for differentiating between lacunar and non-lacunar AIS in a sub-analysis of 239 participants from the cohort, the researchers noted equivalent specificity (89 percent) as well as comparable accuracy rates (89 and 90 percent respectively) and processing time (22 seconds and 23 seconds respectively).

Three Key Takeaways

1. Deep learning efficiency. The use of a deep learning application significantly improves the efficiency of diagnosing acute ischemic stroke (AIS) on brain MRI, offering a high sensitivity (90 percent) and specificity (89 percent) with an average processing time of 24 seconds, which is faster than the traditional T2WI sequences.

2. Comparable diagnostic accuracy. The deep learning application shows comparable sensitivity, specificity, and accuracy rates to traditional T2WI sequences, suggesting that T2WI can be omitted without compromising diagnostic accuracy for AIS.

3. Time-savings in acute care settings. Omitting T2WI sequences in favor of the deep learning application may shorten the detection time for AIS, which could be crucial given the critical time window for stroke intervention. This is particularly important because time delays caused by T2WI may negatively impact patient outcomes.

However, the study authors also found that for non-lacunar AIS, the deep learning application had higher sensitivity (92 percent vs. 85 percent for lacunar AIS) and AUROC rates (96 percent vs. 92 percent). They noted prior research had revealed a slightly declining performance of the deep learning algorithm with respect to decreasing infarct size.

“This is likely attributed to the challenges that emerged when the (the deep learning application) attempted to detect very minor abnormalities. … However, the difference was marginal, so further evaluation is needed to confirm the significance of this difference,” added Kim and colleagues.

(Editor’s note: For related content, see “Can Deep Learning Automate Amyloid Positivity Assessment on Brain PET Imaging?,” “FDA Clears New Version of AI Segmentation Software for Brain MRI” and “Can AI Enhance MRI Detection of Amyloid-Related Abnormalities in Patients with Alzheimer’s Disease?”)

Beyond the inherent limitations of a single-center retrospective study, the authors acknowledged the small cohort (71 patients) utilized to assess the impact of T2WI MRI. They also noted that broader extrapolation of the study findings may be limited as all MRI scans were obtained with 3T devices from a single vendor.