Reworked the flow a bit

ai_for_good.qmd

@@ -100,12 +100,13 @@ A collaborative research team from the University of Khartoum and the ICTP is ex
 
 This portable, self-contained system shows great promise for entomology. The researchers suggest it could revolutionize insect monitoring and vector control strategies in remote areas. By providing cheaper, easier mosquito analytics, TinyML could significantly bolster malaria eradication efforts. Its versatility and minimal power needs make it ideal for field use in isolated, off-grid regions with scarce resources but high disease burden.
 
-### TinyML Design Contest (TDC) - TinyML contest in healthcare
-The first TinyML contest in healthcare, TDC’22 [@jia2023life] was held in 2022 to motivate participating teams to design AI/ML algorithms for detecting life-threatening ventricular arrhythmias (VAs) and deploy them on Implantable Cardioverter Defibrillators (ICDs). VAs are the main cause of sudden cardiac death (SCD). People at high risk of SCD rely on the ICD to deliver proper and in-time defibrillation treatment (i.e., shocking the heart back into normal rhythm) when experiencing life-threatening VAs. On-device algorithm for early and in-time life-threatening VA detection will increase the chances of survival. 
+### TinyML Design Contest in Healthcare
 
-The proposed AI/ML algorithm needs to be deployed and executed on an extremely low-power and resource-constrained microcontroller (MCU) (a $10 development board with an ARM Cortex-M4 core at 80 MHz, 256 kB of flash memory and 64 kB of SRAM). The submitted designs were evaluated by metrics measured on the MCU: (1) detection performance; (2) inference latency; and (3) memory occupation by the program of AI/ML algorithms. The champion, GaTech EIC Lab, obtained 0.972 in $F_β$ (F1 score with a higher weight to recall), 1.747 ms in latency and 26.39 kB in memory footprint with a deep neural network.
+The first TinyML contest in healthcare, TDC’22 [@jia2023life], was held in 2022 to motivate participating teams to design AI/ML algorithms for detecting life-threatening ventricular arrhythmias (VAs) and deploy them on Implantable Cardioverter Defibrillators (ICDs). VAs are the main cause of sudden cardiac death (SCD). People at high risk of SCD rely on the ICD to deliver proper and timely defibrillation treatment (i.e., shocking the heart back into normal rhythm) when experiencing life-threatening VAs. 
 
-An ICD with on-device VA detection algorithm was inplanted in a clinical trial (see the author's [presentation](https://youtu.be/vx2gWzAr85A?t=2359)). 
+An on-device algorithm for early and timely life-threatening VA detection will increase the chances of survival. The proposed AI/ML algorithm needed to be deployed and executed on an extremely low-power and resource-constrained microcontroller (MCU) (a $10 development board with an ARM Cortex-M4 core at 80 MHz, 256 kB of flash memory and 64 kB of SRAM). The submitted designs were evaluated by metrics measured on the MCU for (1) detection performance; (2) inference latency; and (3) memory occupation by the program of AI/ML algorithms. 
+
+The champion, GaTech EIC Lab, obtained 0.972 in $F_\beta$ (F1 score with a higher weight to recall), 1.747 ms in latency and 26.39 kB in memory footprint with a deep neural network. An ICD with an on-device VA detection algorithm was [implanted in a clinical trial](https://youtu.be/vx2gWzAr85A?t=2359).
 
 ## Science