AI – optimized glioblastoma chemotherapy

Pratik Shah, Gregory Yauney,  and MIT Media Lab researchers have developed an AI  model that could make glioblastoma chemotherapy regimens less toxic but still effective. It analyzes current regimens and iteratively adjusts doses to optimize treatment with the lowest possible potency and frequency toreduce tumor sizes.

In simulated trials of 50 patients, the machine-learning model designed treatment cycles that reduced the potency to a quarter or half of the doses It often skipped administration, which were then scheduled twice a year instead of monthly.

Reinforced learning was used to teach the model to favor certain behavior that lead to a desired outcome.  A combination of  temozolomide and procarbazine, lomustine, and vincristine, administered over weeks or months, were studied.

As the model explored the regimen, at each planned dosing interval it decided on actions. It either initiated or withheld a dose. If it administered, it then decided if the entire dose, or a portion, was necessary. It pinged another clinical model with each action to see if the the mean tumor diameter shrunk.

When full doses were given, the model was penalized, so it instead chose fewer, smaller doses. According to Shah, harmful actions were reduced to get to the desired outcome.

The J Crain Venter Institute’s Nicholas Schork said that the model offers a major improvement over the conventional “eye-balling” method of administering doses, observing how patients respond, and adjusting accordingly.


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson – Ed Simcox – Sean Lane

AI predicts drug combination complications

Stanford’s Monica Agrawal  and Jure Leskovecand Marinka Zitnik used AI to study the body’s underlying cellular machinery and predict side effects of drug combinations. There are about 1,000 known side effects and 5,000 drugs on the market, making nearly 125 billion possible side effects between all possible pairs of drugs.

The team created a network describing how the 19,000 proteins in our bodies interact with each other and how different drugs affect them. Using more than 4 million known associations between drugs and side effects, they  designed a method to identify patterns in how side effects arise based on how drugs target different proteins.


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson

REGISTRATION RATES INCREASE JULY 20th

Vinod Khosla on AI in healthcare | ApplySci @ Stanford

Vinod Khosla discussed AI at ApplySci’s recent Wearable Tech + Digital Health + Neurotech conference at Stanford;


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson

REGISTRATION RATES INCREASE JULY 13th

Tony Chahine on human presence, reimagined | ApplySci @ Stanford

Myant‘s Tony Chahine reimagined human presence at ApplySci’s recent Wearable Tech + Digital Health + Neurotech conference at Stanford:


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab.  Speakers include:  Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum – Phillip Alvelda Marom Bikson

REGISTRATION RATES INCREASE JUNE 29TH

Phillip Alvelda: More intelligent; less artificial | ApplySci @ Stanford

Phillip Alvelda discussed AI and the brain at ApplySci’s recent Wearable Tech + Digital Health + Neurotech Silicon Valley conference at Stanford:


Dr. Alvelda will join us again at Wearable Tech + Digital Health + Neurotech Boston, on September 24, 2018 at the MIT Media Lab.  Other speakers include: Rudy Tanzi – Mary Lou Jepsen – George ChurchRoz PicardNathan IntratorKeith JohnsonJuan EnriquezJohn MattisonRoozbeh GhaffariPoppy Crum Marom Bikson

REGISTRATION RATES INCREASE JUNE 22nd

AI CT analysis speeds stroke identification, treatment

Viz.ai‘s algorithms analyze brain scans and immediately transfer data to ensure rapid stroke treatment. The system connects to a hospital CT and sends alerts when a suspected LVO stroke has been identified.  Radiological images are sent to a doctor’s phone.  The company claims that the median time from picture to notification is less than 6 minutes, which can be life-saving, as they also claim that standard stroke workflow is now 66 minutes. Patient transfer to  interventional centers is initiated through messaging and call capabilities connected with emergency and transportation services.


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 24, 2018 at the MIT Media Lab

DARPA’s Justin Sanchez on driving and reshaping biotechnology | ApplySci @ Stanford

DARPA Biological Technologies Office Director Dr. Justin Sanchez on driving and reshaping biotechnology.  Recorded at ApplySci’s Wearable Tech + Digital Health + Neurotech Silicon Valley conference on February 26-27, 2018 at Stanford University.


Join ApplySci at the 9th Wearable Tech + Digital Health + Neurotech Boston conference on September 25, 2018 at the MIT Media Lab

Heart attack, stroke, predicted via retinal images

Google’s Lily Peng has developed an algorithm that can predict heart attacks and strokes by analyzing images of the retina.

The system also shows which eye areas lead to successful predictions, which can provide insight into the causes of cardiovascular disease.

The dataset consisted of 48,101 patients from the UK Biobank database and 236,234 patients from EyePACS database.  A study of  12,026 and 999 patients showed a high level of accuracy:

-Retinal images of a smoker from a non-smoker 71 percent of the time, compared to a ~50 percent human  accuracy.

-While doctors can typically distinguish between the retinal images of patients with severe high blood pressure and normal patients, Google AI’s algorithm predicts the systolic blood pressure within 11 mmHg on average for patients overall, including those with and without high blood pressure.

-According to the company the algorithm predicted direct cardiovascular events “fairly accurately, ” statin that “given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70% of the time. This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol.”

According to Peng: “Given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70 percent of the time, This performance approaches the accuracy of other CV risk calculators that require a blood draw to measure cholesterol.”


Join ApplySci at Wearable Tech + Digital Health + Neurotech Silicon Valley on February 26-27, 2018 at Stanford University. Speakers include:  Vinod Khosla – Justin Sanchez – Brian Otis – Bryan Johnson – Zhenan Bao – Nathan Intrator – Carla Pugh – Jamshid Ghajar – Mark Kendall – Robert Greenberg – Darin Okuda – Jason Heikenfeld – Bob Knight – Phillip Alvelda – Paul Nuyujukian –  Peter Fischer – Tony Chahine – Shahin Farshchi – Ambar Bhattacharyya – Adam D’Augelli – Juan-Pablo Mas – Shreyas Shah– Walter Greenleaf – Jacobo Penide  – Peter Fischer – Ed Boyden

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