3.80.211.101
dgid:
enl:
npi:0
-Advertisement-
-Advertisement-
Conference Roundup

Can deep machine learning accurately diagnose retinopathy of prematurity?

Posted on

A novel deep machine learning algorithm for telemedicine screening was able to accurately detect zone and different stages of retinopathy of prematurity (ROP), according to a study.

Researcher prospectively obtained 1400 consecutive wide-field retinal images of 111 prematurely-born infants, of which 69% (n = 966) had some stage of ROP>

Novel computer-aided approaches were verified for traceability and clinical accuracy using a confusion matrix analysis. Scores for vessel detection were 98.5% for accuracy, 92.9% for sensitivity, 98.6% for specificity, and 74% for F1. ROP stage detection included global accuracy of 98.3%, sensitivity of 99.3%, precision of 98.9%, and F1-score of 99.1%.

Zone detection and detection of plus and pre-plus disease was done with image processing resulting in 95% accuracy.

Reference
Punjabi OS, et al. Retinopathy of prematurity screening using a novel method of advanced image processing and deep machine learning. Presented at: 2020 ASRS Virtual Meeting.

-Advertisement-
Related Articles
Compounded fixed-combination injection provides stable mydriasis during surgery
Aug 04, 2021
More research needed on link between strabismus and mental health disorders in kids
May 21, 2021
Patient demographics influence utilization of care for eye conditions
May 21, 2021
-Advertisement-
-Advertisement-
-Advertisement-
-Advertisement-
-Advertisement-