Effectiveness of Kanna photoscreener in detecting amblyopia risk factors

Abstract

Purpose: Amblyopia is a significant public health problem. Photoscreeners have been shown to have significant potential for screening; however, most are limited by cost and display low accuracy. The purpose of this study was validate a novel artificial intelligence (AI) and machine learning–based facial photoscreener “Kanna,” and to determine its effectiveness in detecting amblyopia risk factors. Methods: A prospective study that included 654 patients aged below 18 years was conducted in our outpatient clinic. Using an android smartphone, three images of each the participants’ face were captured by trained optometrists in dark and ambient light conditions and uploaded onto Kanna. Deep learning was used to create an amblyopia risk score based on our previous study. The algorithm generates a risk dashboard consisting of six values: five normalized risk scores for ptosis, strabismus, hyperopia, myopia and media opacities; and one binary value denoting if a child is “at-risk” or “not at-risk.” The presence of amblyopia risk factors (ARF) as determined on the ophthalmic examination was compared with the Kanna photoscreener. Results: Correlated patient data for 654 participants were analyzed. The mean age of the study population was 7.87 years. The algorithm had an F-score, 85.9%; accuracy, 90.8%; sensitivity, 83.6%; specificity, 94.5%; positive predictive value, 88.4%; and negative predictive value, 91.9% in identifying amblyopia risk factors. The P value for the amblyopia risk calculation was 8.5 × 10 −142 implying strong statistical significance. Conclusion: The Kanna photo-based screener that uses deep learning to analyze photographs is an effective alternative for screening children for amblyopia risk factors.

Global research of artificial intelligence in strabismus: a bibliometric analysis

“…With the prosperity of AI technology, the development direction is changing from digital devices [like photoscreener, eye trackers, virtual reality headsets ( 39 , 44 , 64 )] to automatic screening methods, which is more economical and practical in undeveloped areas. In the future, optimizing algorithms and proposing new techniques will become the focus of this field.…”

Reliability of Smart Phone Photographs for School Eye Screening

“…While screening with photographs may also help identify ocular morbidities other than refractive error, it is difficult to accurately quantify refractive errors based on the present methodology. Artificial and deep machine learning devices such as ‘Kanna’ photoscreeners and smartphone applications such as GoCheckKids have shown promising results in the identification and quantification of refractive errors [ 13 , 26 ]. Demonstrable accuracy achieved with smartphone photography based screening in the present study may pave the way for enhancing the quality of ocular morbidity screening in children by using standardized machine based algorithms.…”

Advances in artificial intelligence models and algorithms in the field of optometry

“…They collected facial images of 54 participants to train and test the model, with results indicating an accuracy of 0.796, sensitivity of 0.882, specificity of 0.756, and an F-score of 0.732. Murali et al (2021) collected facial images of 654 participants (randomly divided into training and verification sets) and constructed a DL model that could screen and identify the risk factors of amblyopia in children based on a convolution neural network. After verification, the values of 0.908, 0.836, and 0.859, respectively, for accuracy, sensitivity, and specificity indicate that the use of DL to analyze photographic images is an effective alternative method for screening risk factors in children with amblyopia.…”