Researchers from Massachusetts General Hospital and MIT stand in front of a CT scanner at MGH, where some of the validation data was generated. Left to right: Regina Barzilay, Lecia Sequist, Florian Fintelmann, Ignacio Fuentes, Peter Mikhael, Stefan Ringer, and Jeremy Wohlwend 

By Alex Ouyang

Deep-learning model takes a personalized approach to assessing each patient’s risk of lung cancer based on CT scans.

The name Sybil has its origins in the oracles of Ancient Greece, also known as sibyls: feminine figures who were relied upon to relay divine knowledge of the unseen and the omnipotent past, present, and future. Now, the name has been excavated from antiquity and bestowed on an artificial intelligence tool for lung cancer risk assessment being developed by researchers at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, Mass General Cancer Center (MGCC), and Chang Gung Memorial Hospital (CGMH).

Lung cancer is the No. 1 deadliest cancer in the world, resulting in 1.7 million deaths worldwide in 2020, killing more people than the next three deadliest cancers combined. 

“It’s the biggest cancer killer because it’s relatively common and relatively hard to treat, especially once it has reached an advanced stage,” says Florian Fintelmann, MGCC thoracic interventional radiologist and co-author on the new work. “In this case, it’s important to know that if you detect lung cancer early, the long-term outcome is significantly better. Your five-year survival rate is closer to 70 percent, whereas if you detect it when it’s advanced, the five-year survival rate is just short of 10 percent.” 

Although there has been a surge in new therapies introduced to combat lung cancer in recent years, the majority of patients with lung cancer still succumb to the disease. Low-dose computed tomography (LDCT) scans of the lung are currently the most common way patients are screened for lung cancer with the hope of finding it in the earliest stages, when it can still be surgically removed. Sybil takes the screening a step further, analyzing the LDCT image data without the assistance of a radiologist to predict the risk of a patient developing a future lung cancer within six years.

By Samson Akintaro

OpenAI, the company behind the popular AI tool ChatGPT, announced on Monday that it has secured an additional investment running into billions of dollars from Microsoft. 

Nairametrics understands that the investment will allow OpenAI to continue its independent research towards developing AI that is increasingly safe, useful, and powerful. 

Note that the latest investment extends the partnership between the two tech companies.

Status quo remains: OpenAI also noted that the investment by Microsoft will not change its status as a capped-profit company, saying: 

• “In pursuit of our mission to ensure advanced AI benefits all of humanity, OpenAI remains a capped-profit company and is governed by the OpenAI non-profit. This structure allows us to raise the capital we need to fulfil our mission without sacrificing our core beliefs about broadly sharing benefits and the need to prioritize safety. 
• “Microsoft shares this vision and our values, and our partnership is instrumental to our progress.” 

Summary: Combining new wearable technology and artificial intelligence, researchers are better able to track motion and monitor the progression of movement disorders.

Source: Imperial College London

A multi-disciplinary team of researchers has developed a way to monitor the progression of movement disorders using motion capture technology and AI.

In two ground-breaking studies, published in Nature Medicine, a cross-disciplinary team of AI and clinical researchers have shown that by combining human movement data gathered from wearable tech with a powerful new medical AI technology they are able to identify clear movement patterns, predict future disease progression and significantly increase the efficiency of clinical trials in two very different rare disorders, Duchenne muscular dystrophy (DMD) and Friedreich’s ataxia (FA).

DMD and FA are rare, degenerative, genetic diseases that affect movement and eventually lead to paralysis. There are currently no cures for either disease, but researchers hope that these results will significantly speed up the search for new treatments.

Tracking the progression of FA and DMD is normally done through intensive testing in a clinical setting. These papers offer a significantly more precise assessment that also increases the accuracy and objectivity of the data collected.

The researchers estimate that using these disease markers mean that significantly fewer patients are required to develop a new drug when compared to current methods. This is particularly important for rare diseases where it can be hard to identify suitable patients.

Scientists hope that as well as using the technology to monitor patients in clinical trials, it could also one day be used to monitor or diagnose a range of common diseases that affect movement behavior such as dementia, stroke and orthopedic conditions.

Senior and corresponding author of both papers, Professor Aldo Faisal, from Imperial College London’s Departments of Bioengineering and Computing, who is also Director of the UKRI Center for Doctoral Training in AI for Healthcare, and the Chair for Digital Health at the University of Bayreuth (Germany), and a UKRI Turing AI Fellowship holder, said, “Our approach gathers huge amounts of data from a person’s full-body movement—more than any neurologist will have the precision or time to observe in a patient.

“Our AI technology builds a digital twin of the patient and allows us to make unprecedented, precise predictions of how an individual patient’s disease will progress.