Kavli Affiliate: Ting Xu
| First 5 Authors: Meng Wang, Tian Lin, Qingshan Hou, Aidi Lin, Jingcheng Wang
| Summary:
Artificial intelligence (AI) shows remarkable potential in medical imaging
diagnostics, yet most current models require retraining when applied across
different clinical settings, limiting their scalability. We introduce
GlobeReady, a clinician-friendly AI platform that enables fundus disease
diagnosis that operates without retraining, fine-tuning, or the needs for
technical expertise. GlobeReady demonstrates high accuracy across imaging
modalities: 93.9-98.5% for 11 fundus diseases using color fundus photographs
(CPFs) and 87.2-92.7% for 15 fundus diseases using optic coherence tomography
(OCT) scans. By leveraging training-free local feature augmentation, GlobeReady
platform effectively mitigates domain shifts across centers and populations,
achieving accuracies of 88.9-97.4% across five centers on average in China,
86.3-96.9% in Vietnam, and 73.4-91.0% in Singapore, and 90.2-98.9% in the UK.
Incorporating a bulit-in confidence-quantifiable diagnostic mechanism further
enhances the platform’s accuracy to 94.9-99.4% with CFPs and 88.2-96.2% with
OCT, while enabling identification of out-of-distribution cases with 86.3%
accuracy across 49 common and rare fundus diseases using CFPs, and 90.6%
accuracy across 13 diseases using OCT. Clinicians from countries rated
GlobeReady highly for usability and clinical relevance (average score 4.6/5).
These findings demonstrate GlobeReady’s robustness, generalizability and
potential to support global ophthalmic care without technical barriers.
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