In Silico Trials of an Open-Source Android-Based Artificial Pancreas: A New Paradigm to Test Safety and Efficacy of Do-It-Yourself Systems

2020 ◽  
Vol 22 (2) ◽  
pp. 112-120 ◽  
Author(s):  
Chiara Toffanin ◽  
Milos Kozak ◽  
Zdenek Sumnik ◽  
Claudio Cobelli ◽  
Lenka Petruzelkova
Keyword(s):  
Open Source ◽  
In Silico ◽  
Artificial Pancreas ◽  
New Paradigm ◽  
Safety And Efficacy ◽  
Do It Yourself

“Going Rogue”

2018 ◽  
Vol 4 (2) ◽  
pp. 137-156
Author(s):  
Samantha D. Gottlieb ◽  
Jonathan Cluck
Keyword(s):  
Open Source ◽  
Treatment Guidelines ◽  
Artificial Pancreas ◽  
Medical Model ◽  
Glucose Monitoring ◽  
Diabetes Research ◽  
Medical Surveillance ◽  
Patient Centered ◽  
Do It Yourself ◽  
Medical Category

Abstract This paper explores our collaborative STS and anthropological project with type 1 diabetes (T1D) hardware “hacking” communities, whose work focuses on reverse-engineering and extracting data from medical devices such as insulin pumps and continuous glucose monitoring systems (CGMS) to create do-it-yourself artificial pancreas systems (APS). Rather than using these devices within their prescriptive and prescribed purposes (surveillance and treatment monitoring), these “hackers” repurpose, reinterpret, and redirect of the possibilities of medical surveillance data in order to reshape their own treatment. Through “deliberate non-compliance” (Scibilia 2017) with cliniciandeveloped treatment guidelines, T1D device hackers deliberatively engage with clinicians’ conceptions and formulations of what constitutes “good treatment” and empower themselves in discussions about the effectiveness of treatment guidelines. Their non-compliance is, however, neither negligence, as implied by the medical category of patients who fail to comply with clinical orders, nor ignorance, but a productive and creative response to their embodied expertise, living with a chronic and potentially deadly condition. Our interlocutors’ explicit connections with the free and open source software principles suggests the formation of a “recursive public” (Kelty 2008) in diabetes research and care practices, from a patient-centered “medical model” to a diverse and divergent patient-led model. The philosophical and ethical underpinnings of the open source and collaborative strategies these patients draw upon radically reshape the principles that drive the commercial health industry and government regulatory structures.

scholarly journals Evolution of Do-It-Yourself Remote Monitoring Technology for Type 1 Diabetes

2020 ◽  
Vol 14 (5) ◽  
pp. 854-859
Author(s):  
Michelle Ng ◽  
Emily Borst ◽  
Ashley Garrity ◽  
Emily Hirschfeld ◽  
Joyce Lee
Keyword(s):  
Type 1 Diabetes ◽  
Open Source ◽  
Remote Monitoring ◽  
Diabetes Management ◽  
Rapid Development ◽  
Artificial Pancreas ◽  
Commercial Space ◽  
Technology Innovations ◽  
Do It Yourself

Background: The Nightscout Project is a leading example of patient-designed, do-it-yourself (DIY), open-source technology innovations to support type 1 diabetes management. We are unaware of studies that have described the evolution of patient-driven innovations from the Nightscout Project to date. Methods: We identified patient-driven, DIY innovations from posts and comments in the “CGM in the Cloud” private Facebook group as well as data from Twitter, GitHub, and the Nightscout website. For each innovation, we described its intent or its unaddressed need as well as the associated features and improvements. We conducted a thematic analysis to identify overarching patterns among the innovations, features, and improvements, and compared the timeline of innovations in the DIY space with the timing of similar innovations in the commercial space. Results: We identified and categorized innovations in Nightscout with the most commonly appearing themes of: visualization improvements, equipment improvements, and user experience improvements. Other emerging themes included: Care Portal support, safety, remote monitoring, decision support, international support, artificial pancreas, pushover notifications, and open-source collaboration. Conclusions: This rapid development of patient-designed DIY innovations driven by unmet needs in the type 1 diabetes community reflects a revolutionary, bottom–up approach to medical innovation. Nightscout users accessed features earlier than if they had waited for commercial products, and they also personalized their tools and devices, empowering them to become the experts of their own care.

scholarly journals Efficient Closed Loop Simulation of Do-It-Yourself Artificial Pancreas Systems

2021 ◽  
pp. 193229682110322
Author(s):  
Jana Schmitzer ◽  
Carolin Strobel ◽  
Ronald Blechschmidt ◽  
Adrian Tappe ◽  
Heiko Peuscher
Keyword(s):  
Real Time ◽  
In Silico ◽  
Closed Loop ◽  
Artificial Pancreas ◽  
Virtual Patients ◽  
Simulation Speed ◽  
Virtual Population ◽  
Speed Up ◽  
Do It Yourself

Background: Numerical simulations, also referred to as in silico trials, are nowadays the first step toward approval of new artificial pancreas (AP) systems. One suitable tool to run such simulations is the UVA/Padova Type 1 Diabetes Metabolic Simulator (T1DMS). It was used by Toffanin et al. to provide data about safety and efficacy of AndroidAPS, one of the most wide-spread do-it-yourself AP systems. However, the setup suffered from slow simulation speed. The objective of this work is to speed up simulation by implementing the algorithm directly in MATLAB®/Simulink®. Method: Firstly, AndroidAPS is re-implemented in MATLAB® and verified. Then, the function is incorporated into T1DMS. To evaluate the new setup, a scenario covering 2 days in real time is run for 30 virtual patients. The results are compared to those presented in the literature. Results: Unit tests and integration tests proved the equivalence of the new implementation and the original AndroidAPS code. Simulation of the scenario required approximately 15 minutes, corresponding to a speed-up factor of roughly 1000 with respect to real time. The results closely resemble those presented by Toffanin et al. Discrepancies were to be expected because a different virtual population was considered. Also, some parameters could not be extracted from and harmonized with the original setup. Conclusions: The new implementation facilitates extensive in silico trials of AndroidAPS due to the significant reduction of runtime. This provides a cheap and fast means to test new versions of the algorithm before they are shared with the community.

An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms

2021 ◽  
pp. 193229682110600
Author(s):  
Ryan Armiger ◽  
Monika Reddy ◽  
Nick S. Oliver ◽  
Pantelis Georgiou ◽  
Pau Herrero
Keyword(s):  
Glycemic Control ◽  
In Silico ◽  
Control Algorithm ◽  
Artificial Pancreas ◽  
Target Range ◽  
Loop Control ◽  
Percentage Time ◽  
Do It Yourself ◽  
Lower Variability ◽  
Baseline Algorithm

Background: User-developed automated insulin delivery systems, also referred to as do-it-yourself artificial pancreas systems (DIY APS), are in use by people living with type 1 diabetes. In this work, we evaluate, in silico, the DIY APS Loop control algorithm and compare it head-to-head with the bio-inspired artificial pancreas (BiAP) controller for which clinical data are available. Methods: The Python version of the Loop control algorithm called PyLoopKit was employed for evaluation purposes. A Python-MATLAB interface was created to integrate PyLoopKit with the UVa-Padova simulator. Two configurations of BiAP (non-adaptive and adaptive) were evaluated. In addition, the Tandem Basal-IQ predictive low-glucose suspend was used as a baseline algorithm. Two scenarios with different levels of variability were used to challenge the algorithms on the adult (n = 10) and adolescent (n = 10) virtual cohorts of the simulator. Results: Both BiAP and Loop improve, or maintain, glycemic control when compared with Basal-IQ. Under the scenario with lower variability, BiAP and Loop perform relatively similarly. However, BiAP, and in particular its adaptive configuration, outperformed Loop in the scenario with higher variability by increasing the percentage time in glucose target range 70-180 mg/dL (BiAP-Adaptive vs Loop vs Basal-IQ) (adults: 89.9% ± 3.2%* vs 79.5% ± 5.3%* vs 67.9% ± 8.3%; adolescents: 74.6 ± 9.5%* vs 53.0% ± 7.7% vs 55.4% ± 12.0%, where * indicates the significance of P < .05 calculated in sequential order) while maintaining the percentage time below range (adults: 0.89% ± 0.37% vs 1.72% ± 1.26% vs 3.41 ± 1.92%; adolescents: 2.87% ± 2.77% vs 4.90% ± 1.92% vs 4.17% ± 2.74%). Conclusions: Both Loop and BiAP algorithms are safe and improve glycemic control when compared, in silico, with Basal-IQ. However, BiAP appears significantly more robust to real-world challenges by outperforming Loop and Basal-IQ in the more challenging scenario.

The Artificial Pancreas in Cyborg Bodies

2020 ◽  
pp. 412-430
Author(s):  
Anthony Ryan Hatch ◽  
Julia T. Gordon ◽  
Sonya R. Sternlieb
Keyword(s):  
Glucose Sensor ◽  
Social Inequalities ◽  
Infusion Pump ◽  
Artificial Pancreas ◽  
Small Scale ◽  
Glucose Levels ◽  
Loop Design ◽  
Access To Health ◽  
Do It Yourself ◽  
And Control

The new artificial pancreas system includes a body-attached blood glucose sensor that tracks glucose levels, a worn insulin infusion pump that communicates with the sensor, and features new software that integrates the two systems. The artificial pancreas is purportedly revolutionary because of its closed-loop design, which means that the machine can give insulin without direct patient intervention. It can read a blood sugar and administer insulin based on an algorithm. But, the hardware for the corporate artificial pancreas is expensive and its software code is closed-access. Yet, well-educated, tech-savvy diabetics have been fashioning their own fully automated do-it-yourself (DIY) artificial pancreases for years, relying on small-scale manufacturing, open-source software, and inventive repurposing of corporate hardware. In this chapter, we trace the corporate and DIY artificial pancreases as they grapple with issues of design and accessibility in a content where not everyone can become a diabetic cyborg. The corporate artificial pancreas offers the cyborg low levels of agency and no ownership and control over his or her own data; it also requires access to health insurance in order to procure and use the technology. The DIY artificial pancreas offers patients a more robust of agency but also requires high levels of intellectual capital to hack the devices and make the system work safely. We argue that efforts to increase agency, radically democratize biotechnology, and expand information ownership in the DIY movement are characterized by ideologies and social inequalities that also define corporate pathways.

scholarly journals The Do-It-Yourself Artificial Pancreas: A Comprehensive Review

2020 ◽  
Vol 11 (6) ◽  
pp. 1217-1235 ◽  
Author(s):  
Jothydev Kesavadev ◽  
Seshadhri Srinivasan ◽  
Banshi Saboo ◽  
Meera Krishna B ◽  
Gopika Krishnan
Keyword(s):  
Artificial Pancreas ◽  
Comprehensive Review ◽  
Do It Yourself

scholarly journals The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day

2018 ◽  
Vol 12 (2) ◽  
pp. 273-281 ◽  
Author(s):  
Roberto Visentin ◽  
Enrique Campos-Náñez ◽  
Michele Schiavon ◽  
Dayu Lv ◽  
Martina Vettoretti ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
In Silico ◽  
Artificial Pancreas ◽  
Real Life ◽  
Glucose Monitoring ◽  
Glucose Variability ◽  
Self Monitoring ◽  
Inhaled Insulin ◽  
Single Meal

Background: A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described. Method: Intraday variability of insulin sensitivity (SI) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily SI. Thus, time-varying distributions of both subject’s basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of “dawn” phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added. Results: One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data. Conclusions: The new modifications introduced in the T1D simulator allow to extend its domain of validity from “single-meal” to “single-day” scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.

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