DeepMind 的 AI 能像医生一样准确地检测出50多种眼部疾病。
The system analyzes 3D scans of the retina and could help speed up diagnoses in hospitals
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Step by step, condition by condition, AI systems are slowly learning to diagnose disease as well as any human doctor, and they could soon be working in a hospital near you. The latest example is from London, where researchers from Google’s DeepMind subsidiary, UCL, and Moorfields Eye Hospital have used deep learning to create software that identifies dozens of common eye diseases from 3D scans and then recommends the patient for treatment.
The work is the result of a multiyear collaboration between the three institutions. And while the software is not ready for clinical use, it could be deployed in hospitals in a matter of years. Those involved in the research described is as “ground-breaking.” Mustafa Suleyman, head of DeepMind Health, said in a press statement that the project was “incredibly exciting” and could, in time, “transform the diagnosis, treatment, and management of patients with sight threatening eye conditions […] around the world.”
The software, described in a paper published in the journal 自然医学, is based on established principles of deep learning, which uses algorithms to identify common patterns in data. In this case, the data is 3D scans of patients’ eyes made using a technique known as optical coherence tomography, or OCT. Creating these scans takes around 10 minutes and involves bouncing near-infrared light off of the interior surfaces of the eye. Doing so creates a 3D image of the tissue, which is a common way to assess eye health. OCT scans are a crucial medical tool, as early identification of eye disease often saves the patient’s sight.
The software was trained on nearly 15,000 OCT scans from some 7,500 patients. These individuals had all been treated at sites operated by Moorfields, which is the largest eye hospital in Europe and North America. The system was fed their scans alongside diagnoses by human doctors. From this, it learned how to first identify the different anatomical elements of the eye (a process known as segmentation) and then recommend clinical action based on the various signs of diseases that the scans show.
In a test where the AI’s judgments were compared with diagnoses by a panel of eight doctors, the software made the same recommendation more than 94 percent of the time.
Whose call is it anyway?
Results like this are extremely encouraging, but experts in the medical community are still worried about how AI systems will be integrated into care practices. Luke Oakden-Rayner, a radiologist who’s written extensively on the subject, says advances in AI are fast pushing us toward a tipping point where software is no longer a tool that’s applied and interpreted by a doctor, but something that makes decisions on behalf of humans.
The first systems are just beginning to cross this line. In April, the FDA approved the first AI-powered program that diagnoses disease without human oversight. As one of the program’s creators put it: “It makes the clinical decision on its own.” (Coincidentally, like today’s new algorithm, this software also analyzes eye scans. But it only looks for one disease, diabetic retinopathy, whereas DeepMind’s is sensitive to more than 50 conditions.)
This is the point at which the risk from medical AI becomes much greater. Our inability to explain exactly how AI systems reach certain decisions is well-documented. And, as we’ve seen with self-driving car crashes, when humans take our hands off the wheel, there’s always a chance that a computer will make a fatal error in judgment.
The researchers from DeepMind, UCL, and Moorfields are aware of these issues, and their software contains a number of features designed to mitigate this sort of problem.
First, the software doesn’t rely on a single algorithm making the decision, but a group of them, and each is trained independently so that any freak error will be overruled by the majority. Second, the system doesn’t just spit out a single answer for each diagnosis. Instead, it gives several possible explanations, alongside its confidence in each one. It also shows how it has labeled the parts of the patient’s eye, giving doctors an opportunity to spot faulty analysis.
But most importantly, the software isn’t a straightforward diagnostic tool. Instead, it’s designed to be used for triage, the process of deciding which patients need care first. So while it does guess what conditions a patient might have, the actual recommendation it makes is how urgently the individual needs to be referred for treatment.
These features sound incidental, but each of them operates like a speed bump, slowing the algorithm down, and giving humans a chance to intervene. The real test, though, will come when this software is deployed and tested in a real clinical environment. When this might happen isn’t known, but DeepMind says it hopes to start the process “soon.”
Gold from the data mine
Along with its clinical possibilities, this research is also interesting as an example of how AI companies benefit from access to valuable datasets. DeepMind, specifically, has been criticized in the past for how it has accessed data from patients treated by the UK’s publicly funded National Health Service (NHS). In 2017, the UK’s data watchdog even ruled that a deal the company struck in 2015 was illegal because it failed to properly notify patients about how their data was being used. (The deal has since been superseded.)
Today’s research would not have been possible without access to this same data. And while the information used in this research was anonymized and patients could opt out, the diagnostic software created from this data belongs solely to DeepMind.
The company says that if the software is approved for use in a clinical setting, it will be provided free of charge to Moorfields’ clinicians for a period of five years. But that doesn’t stop DeepMind from selling the software to other hospitals in the UK or other countries. DeepMind says this sort of deal is standard practice for the industry, and it tells The Verge it “invested significantly” in this research to create the algorithm. It also notes that the data it helped corral is now available for public use and non-commercial medical research.
Despite efforts like this, skepticism about the firm remain. A recent independent panel set up by DeepMind to scrutinize its own business practices suggested that the company needs to be more transparent about its business model and its relationship with Google, which bought the firm in 2014. As DeepMind gets closer to producing commercial products using publicly funded NHS data, this sort of scrutiny will likely become increasingly pointed.
An eye on the future
Regardless of these issues, it’s clear that algorithms like this could be incredibly beneficial. Some 285 million people around the world are estimated to live with a form of sight loss, and eye disease is the biggest cause of this condition.
OCT scans are a great tool for spotting eye disease (5.35 million were performed in the US alone in 2014), but interpreting this data takes time, creating a bottleneck in the diagnostic process. If algorithms can help triage patients by directing doctors to those most in need of care, it could be incredibly beneficial.
As Dr. Pearse Keane, a consultant ophthalmologist at Moorfields who was involved in the research, said in a press statement: “The number of eye scans we’re performing is growing at a pace much faster than human experts are able to interpret them. There is a risk that this may cause delays in the diagnosis and treatment of sight-threatening diseases.
“If we can diagnose and treat eye conditions early, it gives us the best chance of saving people’s sight. With further research it could lead to greater consistency and quality of care for patients with eye problems in the future.”