Viz.ai announced the FDA clearance of its new RV/LV ratio algorithm, adding an important risk stratification feature to its pulmonary embolism AI module, while representing an interesting example of how triage AI solutions might evolve.
Triage + Stratification + Coordination – Viz PE becomes far more comprehensive with its new RV/LV integration, helping radiologists detect/prioritize PE cases and assess right heart strain (a major cause of PE mortality), while equipping PE response teams with more actionable information.
- This addition might also improve clinicians’ experience with Viz PE, noting the risk of developing AI “alert fatigue” when all severity levels are treated the same.
Viz.ai is So On-Trend – Signify Research recently forecast that AI leaders will increasingly expand into new clinical segments, enhance their current solutions, and leverage platform / marketplace strategies, as AI evolves from point solutions to comprehensive workflows. Those trends are certainly evident within Viz.ai’s recent PE strategy…
- Viz PE’s late 2021 launch was a key step in Viz.ai’s expansion beyond neuro/stroke
- Adding RV/LV risk stratification certainly enhances Viz PE’s detection capabilities
- Viz PE was developed by Avicenna.AI, arguably making Viz.ai a platform vendor
- Viz PE’s workflow now combines detection, assessment, and care coordination
The same could be said for Aidoc, which previously added Imbio’s RV/LV algorithm to its PE AI solution (and also supports incidental PE), although few other triage AI workflows are this advanced for PE or other clinical areas.
Viz.ai’s PE and RV/LV integration is a great example of how detection-focused AI tools can evolve through risk/severity stratification and workflow integration — and it might prove to be a key step in Viz.ai’s expansion beyond stroke AI.
A new study out of Cedars Sinai provided what might be the strongest evidence yet that imaging AI triage and prioritization tools can shorten inpatient hospitalizations, potentially bolstering AI’s economic and patient care value propositions outside of the radiology department.
The researchers analyzed patient length of stay (LOS) before and after Cedars Sinai adopted Aidoc’s triage AI solutions for intracranial hemorrhage (Nov 2017) and pulmonary embolism (Dec 2018), using 2016-2019 data from all inpatients who received noncontrast head CTs or chest CTAs.
- ICH Results – Among Cedars Sinai’s 1,718 ICH patients (795 after ICH AI adoption), average LOS dropped by 11.9% from 10.92 to 9.62 days (vs. -5% for other head CT patients).
- PE Results – Among Cedars Sinai’s 400 patients diagnosed with PE (170 after PE AI adoption), average LOS dropped by a massive 26.3% from 7.91 to 5.83 days (vs. +5.2% for other CCTA patients).
- Control Results – Control group patients with hip fractures saw smaller LOS decreases during the respective post-AI periods (-3% & -8.3%), while hospital-wide LOS seemed to trend upward (-2.5% & +10%).
These results were strong enough for the authors to conclude that Cedars Sinai’s LOS improvements were likely “due to the triage software implementation.”
Perhaps more importantly, some could also interpret these LOS reductions as evidence that Cedars Sinai’s triage AI adoption also improved its overall patient care and inpatient operating costs, given how these LOS reductions were likely achieved (faster diagnosis & treatment), the typical associations between hospital long stays and negative outcomes, and the fact that inpatient stays have a significant impact on hospital costs.
A new European Radiology study out of France highlighted how Aidoc’s pulmonary embolism AI solution can serve as a valuable emergency radiology safety net, catching PE cases that otherwise might have been missed and increasing radiologists’ confidence.
Even if that’s technically what PE AI products are supposed to do, studies using commercially available products and focusing on how AI complements radiologists (vs. comparing AI and rad accuracy) are still rare and worth a closer look.
The Diagnostic Study – A team from French telerad provider, IMADIS, analyzed AI and radiologist CTPA interpretations from patients with suspected PE (n = 1,202 patients), finding that:
- Aidoc PE achieved higher sensitivity (0.926 vs. 0.9 AUCs) and negative predictive value (0.986 vs. 0.981 AUCs)
- Radiologists achieved higher specificity (0.991 vs. 0.958 AUCs), positive predictive value (0.95 vs. 0.804 AUCs), and accuracy (0.977 vs. 0.953 AUCs)
- The AI tool flagged 219 suspicious PEs, with 176 true positives, including 19 cases that were missed by radiologists
- The radiologists detected 180 suspicious PEs, with 171 true positives, including 14 cases that were missed by AI
- Aidoc PE would have helped IMADIS catch 285 misdiagnosed PE cases in 2020 based on the above AI-only PE detection ratio (19 per 1,202 patients)
The Radiologist Survey – Nine months after IMADIS implemented Aidoc PE, a survey of its radiologists (n = 79) and a comparison versus its pre-implementation PE CTPAs revealed that:
- 72% of radiologists believed Aidoc PE improved their diagnostic confidence and comfort
- 52% of radiologists the said the AI solution didn’t impact their interpretation times
- 14% indicated that Aidoc PE reduced interpretation times
- 34% of radiologists believed the AI tool added time to their workflow
- The solution actually increased interpretation times by an average of 7.2% (+1:03 minutes)
Now that we’re getting better at not obsessing over AI replacing humans, this is a solid example of how AI can complement radiologists by helping them catch more PE cases and make more confident diagnoses. Some radiologists might be concerned with false positives and added interpretation times, but the authors noted that AI’s PE detection advantages (and the risks of missed PEs) outweigh these potential tradeoffs.