Public articles linked to the same research event.
JMIR Formative Research This exploratory study used GPT-4 to generate and iteratively fine-tune an R script for preprocessing and blob identification/counting in adaptive optics flood illumination ophthalmoscopy (AO-FIO) images, debugging it on images from 4 participants (1 healthy individual and 3 patients with Stargardt disease), having another researcher naive to the prior coding check the code for errors using a different test set of images from 4 other participants (1 healthy individual and 3 patients with Stargardt disease), and comparing cone counts from 5 AO image snippets with counts independently recorded by 2 human graders and those from pre-existing AO analysis software, with the authors positioning the script as functional but nonvalidated.
This exploratory study used GPT-4 to generate and iteratively fine-tune an R script for preprocessing and blob identification/counting in adaptive optics flood illumination ophthalmoscopy (AO-FIO) images, debugging it on images from 4 participants (1 healthy individual and 3 patients with Stargardt disease), having another researcher naive to the prior coding check the code for errors using a different test set of images from 4 other participants (1 healthy individual and 3 patients with Stargardt disease), and comparing cone counts from 5 AO image snippets with counts independently recorded by 2 human graders and those from pre-existing AO analysis software, with the authors positioning the script as functional but nonvalidated.
This exploratory study used GPT-4 to generate and iteratively fine-tune an R script for preprocessing and blob identification/counting in adaptive optics flood illumination ophthalmoscopy (AO-FIO) images, debugging it on images from 4 participants (1 healthy individual and 3 patients with Stargardt disease), having another researcher naive to the prior coding check the code for errors using a different test set of images from 4 other participants (1 healthy individual and 3 patients with Stargardt disease), and comparing cone counts from 5 AO image snippets with counts independently recorded by 2 human graders and those from pre-existing AO analysis software, with the authors positioning the script as functional but nonvalidated.
This exploratory study used GPT-4 to generate and iteratively fine-tune an R script for preprocessing and blob identification/counting in adaptive optics flood illumination ophthalmoscopy (AO-FIO) images, debugging it on images from 4 participants (1 healthy individual and 3 patients with Stargardt disease), having another researcher naive to the prior coding check the code for errors using a different test set of images from 4 other participants (1 healthy individual and 3 patients with Stargardt disease), and comparing cone counts from 5 AO image snippets with counts independently recorded by 2 human graders and those from pre-existing AO analysis software, with the authors positioning the script as functional but nonvalidated.