Teleradiology AI’s Mixed Bag

An AI algorithm that examined teleradiology studies for signs of intracranial hemorrhage had mixed performance in a new study in Radiology: Artificial Intelligence. AI helped detect ICH cases that might have been missed, but false positives slowed radiologists down. 

AI is being touted as a tool that can detect unseen pathology and speed up the workflow of radiologists facing an environment of limited resources and growing image volume.

  • This dynamic is particularly evident at teleradiology practices, which frequently see high volumes during off-hour shifts; indeed, a recent study found that telerad cases had higher rates of patient death and more malpractice claims than cases read by traditional radiology practices.

So teleradiologists could use a bit more help. In the new study, researchers from the VA’s National Teleradiology Program assessed Avicenna.ai’s CINA v1.0 algorithm for detecting ICH on STAT non-contrast head CT studies.

  • AI was used to analyze 58.3k CT exams processed by the teleradiology service from January 2023 to February 2024, with a 2.7% prevalence of ICH.

Results were as follows

  • AI flagged 5.7k studies as positive for acute ICH and 52.7k as negative
  • Final radiology reports confirmed that 1.2k exams were true positives for a sensitivity of 76% and a positive predictive value of 21%
  • There were 384 false negatives (missed ICH cases), for a specificity of 92% and a negative predictive value of 99.3%
  • The algorithm’s performance at the VA was a bit lower than in previously published literature
  • Cases that the algorithm falsely flagged as positive took over a minute longer to interpret than prior to AI deployment
  • Overall, case interpretation times were slightly lower after AI than before

One issue to note is that the CINA algorithm is not intended for small hemorrhages with volumes < 3 mL; the researchers did not exclude these cases from their analysis, which could have reduced its performance.

  • Also, at 2.7% the VA’s teleradiology program ICH prevalence was lower than the 10% prevalence Avicenna has used to rate its performance.

The Takeaway

The new findings aren’t exactly a slam dunk for AI in the teleradiology setting, but in terms of real-world results they are exactly what’s needed to assess the true value of the technology compared to outcomes in more tightly controlled environments.

Viz.ai’s Care Coordination Expansion

Viz.ai advanced its care coordination strategy last week, launching new Pulmonary Embolism and Aortic Disease modules, and unveiling its forthcoming Viz ANX cerebral aneurysm module.

PE & Aortic Modules – The new PE and Aortic modules use AI to quickly detect pulmonary embolisms and aortic dissection in CTA scans, and then coordinate care using Viz.ai’s 3D mobile viewer and clinical communications workflows. It appears that Viz.ai partnered with Avicenna.AI to create these modules, representing a logical way for Viz.ai to quickly expand its portfolio.

Viz ANX Module – The forthcoming Viz ANX module will use the 510k-pending Viz ANX algorithm to automatically detect suspected cerebral aneurysms in CTAs, and then leverage the Viz Platform for care coordination.

Viz.ai’s Care Coordination Strategy – Viz.ai called itself “the leader in AI-powered care coordination” a total of six times in these two announcements, and the company has definitely earned this title for stroke detection/coordination. Adding new modules to the Viz Platform is how Viz.ai could earn “leadership” status across all other imaging-detected emergent conditions.

The Takeaway – Viz.ai’s stroke detection/coordination platform has been among the biggest imaging AI success stories, making its efforts to expand to new AI-based detection and care coordination areas notable (and pretty smart). These module launches are also an example of diagnostic AI’s growing role throughout care pathways, showing how AI can add clinical value beyond the reading room.

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