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Collaborative book Machine Learning Systems
https://harvard-edge.github.io/cs249r_book/
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New version of the AI for Good chapter #32

Closed marcozennaro closed 8 months ago

happyappledog commented 7 months ago

it's very hard to find on-device medical AI cases. I am amazed you found these examples! I found another on-device example if you want to broaden the use cases (they are not necessary for low resource tho): Implantable cardioverter-defibrillator with on-device ML to detect ventricular arrhythmia

https://youtu.be/vx2gWzAr85A?t=2359

happyappledog commented 7 months ago

https://www.nature.com/articles/s42256-023-00659-9

Ventricular fibrillation and ventricular tachycardia are life-threatening ventricular arrhythmias (VAs) and the primary causes of sudden cardiac death, resulting in significant morbidity and mortality1. Individuals at high risk of sudden cardiac death rely on implantable cardioverter–defibrillators (ICDs) to provide timely and appropriate defibrillation treatment in case of life-threatening VAs1. However, existing industry practice is simple rule-based detection methods, which have not been updated over the past few decades2,

TDC’22 designated the NUCLEO-L432KC development kit (STMicroelectronics) as the targeting MCU platform for all participating teams. This $10 development board is equipped with an ARM Cortex-M4 core at 80 MHz, 256 kB of flash memory and 64 kB of SRAM, and its power consumption is around 30 mW in operation and 1.5 mW when idling. The IEGMs data were collected and provided by Singular Medical using ICDs... With the training dataset and unified evaluation platform, participating teams could utilize either the existing frameworks or their own tools to develop, train and deploy the AI/ML algorithm on board with cross-layer optimizations.

profvjreddi commented 7 months ago

Emma, cool. Thanks!

Do you think you could help add this idea of competitions into the material and link to this work please?

On Sun, Nov 5, 2023 at 10:40 PM happyappledog @.***> wrote:

https://www.nature.com/articles/s42256-023-00659-9

TDC’22 designated the NUCLEO-L432KC development kit (STMicroelectronics) as the targeting MCU platform for all participating teams. This $10 development board is equipped with an ARM Cortex-M4 core at 80 MHz, 256 kB of flash memory and 64 kB of SRAM, and its power consumption is around 30 mW in operation and 1.5 mW when idling. The IEGMs data were collected and provided by Singular Medical using ICDs... With the training dataset and unified evaluation platform, participating teams could utilize either the existing frameworks or their own tools to develop, train and deploy the AI/ML algorithm on board with cross-layer optimizations.

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