An artificial intelligence platform for the multihospital collaborative management of congenital cataracts

By Erping Long, Haotian Lin, Zhenzhen Liu, Xiaohang Wu, Liming Wang, Jiewei Jiang, Yingying An, Zhuoling Lin, Xiao-yan Li, Jingjing Chen and 6 more. Nature Biomedical Engineering, 2017

300 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Health Informatics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/dc1eb76fdd0c39c8e53985a447a5f049f860a7e9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yilong Wang
2. Yi Dong
3. Hui Zhi
4. Yongjun Wang
5. Xuan Zhang
6. Yong Jiang
7. Mustafa Suleyman
8. Sufeng Ma

In Radiology, Nuclear Medicine and Imaging

1. Dominic King
2. Alan Karthikesalingam

Citations in the corpus

• Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography (2020, background): “Recent studies demonstrated the potential of integrating AI into both the eye and childhood-disease diagnostic systems and found that it significantly improved clinical diagnostic efficiency and accuracy (Burlina et al., 2017; Kermany et al., 2018; Long et al., 2017; van Grinsven et al., 2016).”
• Artificial intelligence in healthcare: past, present and future (2017, background): “Long et al used it to diagnose congenital cataract disease through learning the ocular images.(24) The CNN yields over 90% accuracy on diagnosis and treatment suggestion.”
• Key challenges for delivering clinical impact with artificial intelligence (2019, methodology): “The limited number of prospective studies to date include diabetic retinopathy grading [48 – 50], detection of breast cancer metastases in sentinel lymph node biopsies [51, 52], wrist fracture detection [53], colonic polyp detection [28, 54], and detection of congenital cataracts [55].”
• Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review (2019, methodology): “The other ophthalmological study [12] focusing on congenital cataracts employed a 3-stage training procedure (ie, identification, evaluation, and strategist networks) to establish a collaborative disease management system beyond only disease identification.”