Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images.

BACKGROUND: Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserving patient privacy. METHODS: A web-based course was designed using 600 synthetic ultra-widefield (UWF) retinal images to teach students to detect disease in these images. The images were generated by stable diffusion, a large generative foundation model, which we fine-tuned with 6285 real UWF images from six categories: five retinal diseases (age-related macular degeneration, glaucoma, diabetic retinopathy, retinal detachment and retinal vein occlusion) and normal. 161 trainee orthoptists took the course. They were evaluated with two tests: one consisting of UWF images and another of standard field (SF) images, which the students had not encountered in the course. Both tests contained 120 real patient images, 20 per category. The students took both tests once before and after training, with a cool-off period in between. RESULTS: On average, students completed the course in 53 min, significantly improving their diagnostic accuracy. For UWF images, student accuracy increased from 43.6% to 74.1% (p<0.0001 by paired t-test), nearly matching the previously published state-of-the-art AI model's accuracy of 73.3%. For SF images, student accuracy rose from 42.7% to 68.7% (p<0.0001), surpassing the state-of-the-art AI model's 40%. CONCLUSION: Synthetic images can be used effectively in medical education. We also found that humans are more robust to novel situations than AI models, thus showcasing human judgement's essential role in medical diagnosis.

Orbital fibrous capsule formation with abscess found 3 years after penetrating injury from a wooden chopstick.

Intraorbital wooden foreign bodies are sometimes difficult to diagnose because of nonspecific clinical manifestations and diversity of imaging characteristics. We herein report a case involving a 72-year-old woman with a history of trauma induced by a coated wooden chopstick 3 years prior. Two years after the incident, computed tomography (CT) scan revealed an intraorbital mass that was initially diagnosed as an intraorbital hemangioma. The patient presented with hyperemia, impairment of ocular movement, and optic neuropathy in her right eye. Magnetic resonance imaging (MRI) showed granulation tissue and an abscess around a foreign body, which was compressing the eyeball. Surgical extraction of the foreign body was performed, leading to resolution of symptoms. The depiction of wooden foreign bodies by imaging is complicated and affected by several factors, increasing the risk of delayed diagnosis. To avoid permanent sequelae, MRI might be helpful because its imaging capabilities are superior to those of CT.