Computer vision using deep learning architecture is broadly used in auto-recognition. In the research, the deep learning model which is trained by categorized single-eye images is applied to achieve the good performance of the model in blepharoptosis auto-diagnosis.
This auto-diagnosis system of blepharoptosis using machine learning architecture will assist in telemedicine, such as early screening of childhood ptosis for prompt referral and treatment. People could use this software via mobile devices to get a primitive diagnosis before they reach the physicians. Furthermore, in primary health care, where there is no oculoplastic surgeon, the software could assist primary care physicians or general ophthalmologists, in identifying the need for a referral.
Study Type
OBSERVATIONAL
Enrollment
17,932
National Taiwan University Hospital
Taipei, Taiwan
The model performance is evaluated by accuracy
An Artificial Intelligence Approach
Time frame: Through study completion, an average of 1 year
AUC (Area Under the Curve)
An Artificial Intelligence Approach
Time frame: Through study completion, an average of 1 year
ROC (Receiver Operating Characteristics) curve.
An Artificial Intelligence Approach
Time frame: Through study completion, an average of 1 year
An Artificial Intelligence Approach to Identifying Facial, Periocular, and Orbital Diseases
The model interpretability is accessed by Grad-CAM (Class Activation Maps).
Time frame: Through study completion, an average of 1 year
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