今日推荐开源项目：《中文OCR:chineseocr_lite》
今日推荐英文原文：《Meet Your Match: AI Finds the Right Clinical Trial for Cancer Patients》
今日推荐开源项目：《中文OCR:chineseocr_lite》传送门：GitHub链接
推荐理由：超轻量级中文ocr，支持竖排文字识别, 支持ncnn推理 , psenet(8.5M) + crnn(6.3M) + anglenet(1.5M) 总模型仅17M. 轻量,有效解决痛点.
今日推荐英文原文：《Meet Your Match: AI Finds the Right Clinical Trial for Cancer Patients》作者：ISHA SALIAN
原文链接：https://blogs.nvidia.com/blog/2020/03/08/intrepida-ai-cancer-clinical-trials/
推荐理由： 这篇文章为我们简单的介绍了人工智能为癌症患者找到正确的临床试验的过程,可以让我们更好的了解这些前沿技术的具体应用.
Meet Your Match: AI Finds the Right Clinical Trial for Cancer Patients
Clinical trials need a matchmaker.Healthcare researchers and pharmaceutical companies rely on trials to validate new, potentially life-saving therapies for cancer and other serious conditions. But fewer than 10 percent of cancer patients participate in clinical trials, and four out of five studies are delayed due to the challenges involved in recruiting participants.
For patients interested in participating in trials, there’s no easy way to determine which they’re eligible for. AI tool Ancora aims to improve the matchmaking process, using natural language processing models to pair patients with potential studies.
“This all started because my friend’s parent was diagnosed with stage 3 cancer,” said Danielle Ralic, founder and CEO of Intrepida, the Switzerland-based startup behind Ancora. “I knew there were trials out there, but when I tried to help them find options, it was so hard.”
The U.S. National Institutes of Health maintains a database of hundreds of thousands of clinical trials. Each study lists a detailed series of text-based requirements, known as inclusion and exclusion criteria, for trial participants.
While users can sort by condition and basic demographics, there may still be hundreds of studies to manually sort through — a time-consuming process of weeding through complex medical terminology.
Intrepida’s customized natural language processing models do the painstaking work of interpreting these text-heavy criteria for patients and physicians, processing new studies on NVIDIA GPUs. The studies listed in the Ancora tool are updated weekly, and users can fill out a simple, targeted questionnaire to shortlist suitable clinical trials, and receive alerts for new potential studies.
“We assessed what 20 questions we could ask that can most effectively knock a patient’s list down from, for example, 250 possible trials to 10,” Ralic said. The platform also shows patients useful information to help decide on a trial, such as how the treatment will be administered, and if it’s been approved in the past to treat other conditions.
Intrepida’s tool is currently available for breast and lung cancer patients. A physician version will soon be available to help doctors find trials for their patients. The company is a member of the NVIDIA Inception virtual accelerator program, which provides go-to-market support for AI startups — including NVIDIA Deep Learning Institute credits, marketing support and preferred pricing on hardware.
Finding the Perfect Match
Intrepida founder Danielle Ralic
Though the primary way patients hear about clinical trials is from their physicians, less than a quarter of patients hear about trials as an option from their doctors, who have limited time and resources to keep track of existing trials.
Ralic recalls being surprised to meet a stage 4 cancer survivor while hiking in Patagonia, and finding out the man had participated in a clinical trial for a new breakthrough drug.
“I asked him, how did you know about the trial? And he said he found out through a relative of his wife’s friend. That’s not how this should work,” Ralic said.
For physicians and patients, a better and more democratized way to discover clinical trials could lead to life-saving results. It could also speed up the research cycle by improving trial enrollment rates, helping pharmaceutical companies more quickly validate new drugs and bring them to market.
As members of the NVIDIA Inception program, Ralic says she and the Intrepida team were able to meet with other AI startups and with NVIDIA developers at the GPU Technology Conference held in Munich in 2018.
“We joined the program because, as a company that was working with NVIDIA GPUs already, we wanted to develop more sophisticated natural language models,” she said. “There’s been a lot to learn from NVIDIA team members and other Inception startups.”
Using NVIDIA GPUs has enabled Intrepida to shrink training times for one epoch from 20 minutes to just 12 seconds.
Diversifying the Data
A female startup founder in an industry that to date has been dominated by men, Ralic says more diversity is key to improving the healthcare industry as a whole — and especially clinical trials.
“Healthcare is holistic. It involves so many different types of people and knowledge,” she said. “Without a diversity of perspectives, we can never address the problems the healthcare industry has.”
The data backs her up. Clinical trial participants in the United States skew overwhelmingly white and male. The lack of diversity in trials can lead to critical errors in drug dosage.
For example, in 2013, the U.S. Food and Drug Administration mandated doses for several sleeping aids to be cut in half for women. Because females metabolize the drug differently, it increased their risk of getting in a car accident the morning after taking a sleeping pill.
“If we don’t have a diverse trial population, we won’t know whether a patient of a different gender or ethnicity will react differently to a new drug,” Ralic said. “If we did it right from the start, we could improve how we prescribe medicine to people, because we’re all different.”
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