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Diabetes Has a New Enemy: Robo-Pancreas

Diabetes Has a New Enemy Robo Pancreas IEEE Spectrum

Diabetes Has a New Enemy Robo Pancreas IEEE Spectrum


Now, after half a century of work, a solution at last is in the offing: the artificial pancreas. It links data from an implanted blood-sugar sensor to a computer, which then controls how a pump worn on the hip dribbles insulin under the skin through a pipette. In its fully realized form, the machine would take the patient out of the decision-making loop, which is why it is often called a closed-loop system.

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A slew of improvements in sensors, actuators, algorithms, and insulin are coming together to create the artificial pancreas:

“We used a Dexcom continuous glucose sensor, hooked through a cellphone with an algorithm and to a Roche pump, linked to it by a Bluetooth signal,” says Herrick, recalling each detail as if the trial had just happened. “One night my blood sugar was 80 [milligrams of glucose per deciliter of blood], with an arrow pointing downward—it was dropping at 2 milligrams of glucose per deciliter per minute. The system shut off insulin, the blood sugar cruised down to 78, sat there, rose to 110, and then, no more movement. It was level.” Normal fasting blood glucose ranges from 70 to 100 mg/dl.


It’s not completely automated, like the trial system was, but such continuous monitoring is itself a huge advance over 10 years ago, and one that lends itself to remote monitoring through the cloud. Last year, Dexcom got FDA approval for its Dexcom G4 Platinum System with Share, which parents can use to keep tabs on their kids’ blood sugar.

There's still a lot of work to be done but this is very promising!

I like the comparison to the development of the self-driving car:

The development of the technology has proceeded by measured steps, much like the progress toward the driverless car—first antilock brakes, next GPS navigation, then adaptive cruise control and self-parking. Finally, at the end of the rainbow, the Google self-driving car. The first step toward a robotic pancreas came in 1964, when a hospital-based experiment proved, in principle, that it was possible to achieve near-normal blood-sugar control. In the 1970s, Dean Kamen invented the insulin pump, making it possible for patients to administer insulin to themselves in a continuous fashion, rather than through frequent injections. Soon after, a hospital system called Biostator GCIIS was released in Germany; it combined a pump with a large, complex continuous glucose monitor.

In recent years, pumps have become smaller, more reliable, more programmable, and more comfortable, using ever-finer pipettes, which the patient inserts through a slightly larger needle. Continuous glucose monitors were first approved a decade ago, and they are beginning to replace the finger-prick method, now that improved coatings and other engineering details have allowed patients to keep their superthin electrochemical sensors under the skin for seven days.

There's a really cool big data problem to solve with the artificial pancreas:

Besides the sensor and the pump are the algorithms, the secret sauce that allows the artificial pancreas to analyze, learn, and control. One of the first algorithmic techniques looked at the rate of change of blood sugar. Another one, sometimes called an expert system, sets up a table that pairs problems with responses in the form “if this happens, do that; if that happens, do this,” says Aaron Kowalski, a medical geneticist who heads artificial-pancreas research for the JDRF.

A third kind of algorithm tries to model human physiology, for instance by considering how quickly food passes through your system and how long the insulin takes to work. “The beauty of this approach is that it’s like chess programming: You reset the variable when your opponent makes his move—that is, when new data arrive,” says Kowalski.

Tuning these algorithms requires big data, gathered from both the individual patient and the larger community of patients. Hovorka’s group at the University of Cambridge is conducting trials of advanced systems in the home, not just in controlled settings. Hovorka is also working with a corporate partner, but he won’t say which one. (He notes, however, that only two companies are pushing the closed-loop solution now: Medtronic and Animas Corp., in West Chester, Pa.) He says his algorithms learn by doing and so adapt to the patient.

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