Category: Artificial Intelligence

Aidoc & Riverain’s Platform Partnership

Posted on by Jake Fishman

Aidoc and Riverain Technologies announced a new partnership that will make Riverain’s ClearRead CT and ClearRead Xray solutions available on the Aidoc platform, while advancing the companies’ respective platform strategies. 

The Chest AI Package – In addition to offering Riverain’s AI tools individually, Aidoc will provide them as part of an ‘integrated chest AI package’ that also includes Aidoc’s modules for PE, incidental PE, and rib fractures. 

Riverain’s Platform Push – Riverain has amassed a solid network of AI marketplace and OEM partners over the years, and it now appears to be expanding its channel to complementary AI vendors. Riverain’s new Aidoc alliance comes just a few weeks after a similar partnership with Volpara that combines ClearRead CT with the Volpara Lung platform.

Aidoc’s Platform Portfolio – After years of building out its homegrown AI portfolio (7 products) and customer base (600 health centers), Aidoc is evolving into an AI platform company. Over the last year, Aidoc has assembled a solid AI portfolio that combines its own triage products with solutions that it doesn’t offer (Imbio, Icometrix, Subtle Medical, Riverain), allowing its clients to expand their AI stack without overhauling their infrastructure with each new tool.

The Takeaway – We’re at an interesting time in the AI space with a small handful of diversified AI players (e.g. Aidoc, Qure.ai), a group of focused category leaders (e.g. Riverain w/ thoracic, ScreenPoint w/ mammography), and an AI customer base that would prefer not to support multiple AI infrastructures. Although marketplaces also solve this problem, it’s easy to see how complementary vendor partnerships like these could play a growing role in how AI is delivered going forward.

Aidoc and Subtle Medical’s End-to-End Alliance

Posted on by Jake Fishman

Aidoc and Subtle Medical launched an interesting new partnership that will make Subtle’s image acquisition / enhancement software available on the Aidoc AI platform.

End-to-End Partnership – The addition of SubtlePET and SubtleMR to the Aidoc AI platform will create what Aidoc called an “end-to-end” solution and “the first joint offering of AI for both image acquisition and triage.” Some folks might mistake that to mean that they will create new combined image acquisition+triage solutions, but they won’t be specifically linked (Aidoc doesn’t have MRI or PET tools yet anyway).

Aidoc, a Platform Company – Aidoc seems to be increasingly positioning itself as an AI platform company, which is an understandable strategy given users’ need for comprehensive / consistent AI workflows. Aidoc’s initial partnerships also allow the triage-focused vendor to offer a far more comprehensive value proposition (Subtle for acquisition, icometrix for stroke analysis/assessment).

Subtle Upsides – The alliance introduces Subtle Medical to Aidoc’s sizable list of clients (used at >500 medical centers, a high profile partnership w/ RP), and adds to Subtle’s current alliances with AI marketplace vendors (e.g. Blackford, Nuance, Incepto) and complementary solutions companies (e.g. Cortechs.ai).

The Takeaway – Although AI platform alliance stories don’t usually earn a spot at the top of The Imaging Wire, this alliance is pretty notable given what it suggests about Aidoc’s AI platform strategy and about the growing trend towards complementary AI alliances. It’s also a nice way for Subtle Medical to expand its reach.

Bad AI Goes Viral

Posted on by Jake Fishman

A recent mammography AI study review quickly evolved from a “study” to a “story” after a single tweet from Eric Topol (to his 521k followers), calling mammography AI’s accuracy “very disappointing” and prompting a new flow of online conversations about how far imaging AI is from achieving its promise. However, the bigger “story” here might actually be how much AI research needs to evolve.

The Study Review: A team of UK-based researchers reviewed 12 digital mammography screening AI studies (n = 131,822 women). The studies analyzed DM screening AI’s performance when used as a standalone system (5 studies), as a reader aid (3 studies), or for triage (4 studies).

The AI Assessment: The biggest public takeaway was that 34 of the 36 AI systems (94%) evaluated in three of the studies were less accurate than a single radiologist, and all were less accurate than the consensus of two or more radiologists. They also found that AI modestly improved radiologist accuracy when used as a reader aid and eliminated around half of negative screenings when used for triage (but also missed some cancers).

The AI Research Assessment: Each of the reviewed studies were “of poor methodological quality,” all were retrospective, and most studies had high risks of bias and high applicability concerns. Unsurprisingly, these methodology-focused assessments didn’t get much public attention.

The Two Takeaways: The authors correctly concluded that these 12 poor-quality studies found DM screening AI to be inaccurate, and called for better quality research so we can properly judge DM screening AI’s actual accuracy and most effective use cases (and then improve it). However, the takeaway for many folks was that mammography screening AI is worse than radiologists and shouldn’t replace them, which might be true, but isn’t very scientifically helpful.

Unsupervised COVID AI

Posted on by Jake Fishman

MGH’s new pix2surv AI system can accurately predict COVID outcomes from chest CTs, and it uses an unsupervised design that appears to solve some major COVID AI training and performance challenges.

Background – COVID AI hasn’t exactly earned the best reputation (short history + high annotation labor > leading to bad data > creating generalization issues), limiting most real world COVID analysis to logistic regression.

Designing pix2surv – pix2surv’s weakly unsupervised design and use of a generative adversarial network avoids these COVID AI pitfalls. It was directly trained with CTs from MGH’s COVID workflow (no labeling, no supervised training) and accurately estimates patient outcomes directly from their chest CTs.

pix2surv Performance – pix2surv accurately predicted the time of each patient’s ICU admission or death and applied the same analysis to stratify patients into high and low-risk groups. More notably, it “significantly outperformed” current laboratory tests and image-based methods with both predictions.

Applications – The MGH researchers believe pix2surv can be expanded to other COVID use cases (e.g. predicting Long COVID), as well as “other diseases” that are commonly diagnosed in medical images and might be hindered by annotation labor.

The Takeaway – pix2surv will require a lot more testing, and its chance of maintaining this type of performance across other sites and diseases might be a longshot (at least right away). However, pix2surv’s streamlined training and initial results are notable, and it would be very significant if a network like this was able to bring pattern-based unsupervised AI into clinical use.

Veye Validation

Posted on by Jake Fishman

A team of Dutch radiologists analyzed Aidence’s Veye Chest lung nodule detection tool, finding that it works “very well,” while outlining some areas for improvement.

The Study – After using Veye Chest for 1.5 years, the researchers analyzed 145 chest CTs with the AI tool and compared its performance against three radiologists’ consensus reads, finding that:

  • Veye Chest detected 130 nodules (80 true positive, 11 false negative, 39 false positives)
  • That’s 88% sensitivity, a 1.04 mean FP per-scan rate, and 95% negative predictive value
  • The radiologists and Veye Chest had different size measurements for 23 nodules
  • Veye Chest tended to overestimate nodule size (bigger than rads w/ 19 of the 23)
  • Veye Chest and the rads’ nodule composition measurements had a 95% agreement rate

The Verdict – The researchers found that Veye Chest “performs very well” and matched Aidence’s specifications. They also noted that the tool is “not good enough to replace the radiologist” and its nodule size overestimations could lead to unnecessary follow-up exams.

The Takeaway – This is a pretty positive study, considering how poorly many recent commercial AI studies have gone and understanding that no AI vendor would dare propose that their AI tools “replace the radiologist.” Plus, it provides the feedback that Aidence and other AI developers need to keep getting better. Given the lack of AI clinical evidence, let’s hope we see a lot more studies like this.

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