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COMMENTARY
Year : 2020  |  Volume : 68  |  Issue : 7  |  Page : 1411

Commentary: How useful is a deep learning smartphone application for screening for amblyogenic risk factors?


Institute of Ophthalmology, JN Medical College, Aligarh Muslim University, Aligarh, Uttar Pradesh, India

Date of Web Publication25-Jun-2020

Correspondence Address:
Prof. Abadan K Amitava
Institute of Ophthalmology, JN Medical College, Aligarh Muslim University, Aligarh - 202 001, Uttar Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijo.IJO_1900_20

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How to cite this article:
Amitava AK. Commentary: How useful is a deep learning smartphone application for screening for amblyogenic risk factors?. Indian J Ophthalmol 2020;68:1411

How to cite this URL:
Amitava AK. Commentary: How useful is a deep learning smartphone application for screening for amblyogenic risk factors?. Indian J Ophthalmol [serial online] 2020 [cited 2020 Jul 13];68:1411. Available from: http://www.ijo.in/text.asp?2020/68/7/1411/287544



As elsewhere artificial intelligence (AI) seems to be replacing human endeavor. Recall the recent docking of the Space-X Dragon module with the International Space Station, Tesla's self-driving cars, or the targeted advertisements encroaching your view while browsing: all examples of AI at work.

AI is often spoken with machine learning (ML) and deep learning (DL): the former uses algorithms applied to huge data sets, to analyze and learn patterns to make informed predictions. In case of errors, human experts step in. DL, in contrast, uses a layered algorithmic structure, appropriately labelled an artificial neural network, which senses the inaccuracy if any and auto-course corrects.

As Ting recently pointed out, the advanced mathematical models along with access to big data, has permitted AI to enlarge its foot print into healthcare.[1] DL has demonstrated its capability in image, speech, and motion recognition, understandably impacting medical specialties like radiology, dermatology, and pathology. In ophthalmology, AI-based equipment has successfully shown its functionality while evaluating fundus images for glaucoma, and macular degeneration and diabetic retinopathy.[2] Li et al. developed and evaluated an OCT trained DL technique – OCTD-Net, to detect early DR.[3] They reported meaningful accuracy, sensitivity, and specificity of 0.92, 0.90, and 0.95 for grade 1, though not for 0. Importantly in comparison studies, for predicting glaucomatous optic neuropathy, Jamal et al. pitted a DL trained with RNFL-thickness parameters from SD-OCT against two glaucoma specialists: DL performed significantly better on Spearman's correlations with standard automated perimetry: roh of 0.54 Vs 0.48, at P < 0.001; and on partial AUC, for predicting GON: 0.529 vs 0.411, P = 0.016.[4]

In this context, the authors need to be commended in demonstrating the use of DL for screening for amblyogenic risk factors (ARFs) using an android based smartphone.[5] Yet it needs to stand upto ordinary digital cameras,[6] smartphones,[7] and even the retinoscope all using the Bruckner's reflex.[8] Moreover the study has no comparator group, and has perhaps merely demonstrated the capability of using this approach on 18–23 year-old optometry students: it needs to be whetted on 4–7 year olds, when anti-amblyopia measures can be effective. Interestingly as an exercise, if we draw up a 2 × 2 table, and plug in the sensitivity (88.2%) and specificity (75.6%) values, and imagine screening a thousand children, it yields a false positive rate of around 83%: 228 of 272 who would test positive. The magic of the paper lies in the novel approach using AI to demonstrate functionality: How well it will perform in a realistic environment remains a moot question.



 
  References Top

1.
Sabanayagam C, Xu D, Ting DSW, Nusinovici S, Banu R, Hamzah H, et al. A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations. Lancet Digit Heal 2020;2:e295-302.  Back to cited text no. 1
    
2.
Sengupta S, Singh A, Leopold HA, Gulati T, Lakshminarayanan V. Ophthalmic diagnosis using deep learning with fundus images – A critical review. Artif Intell Med 2020;102:101758.  Back to cited text no. 2
    
3.
Li X, Shen L, Shen M, Tan F, Qiu CS. Deep learning based early stage diabetic retinopathy detection using optical coherence tomography. Neurocomputing 2019;369:134-44.  Back to cited text no. 3
    
4.
Jammal AA, Thompson AC, Mariottoni EB, Berchuck SI, Urata CN, Estrela T, et al. Human versus machine: Comparing a deep learning algorithm to human gradings for detecting glaucoma on fundus photographs. Am J Ophthalmol 2020;211:123-31.  Back to cited text no. 4
    
5.
Murali K, Krishna V, Krishna V, Kumari B.Application of deep learning and image processing analysis of photographs for amblyopia screening. Indian J Ophthalmol 2020;68;1407-10.  Back to cited text no. 5
    
6.
Bani SAO, Amitava AK, Sharma R, Danish A. Beyond photography: Evaluation of the consumer digital camera to identify strabismus and anisometropia by analyzing the Bruckner's reflex. Indian J Ophthalmol 2013;61:608-11.  Back to cited text no. 6
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7.
Arnold RW, O'Neil JW, Cooper KL, Silbert DI, Donahue SP. Evaluation of a smartphone photoscreening app to detect refractive amblyopia risk factors in children aged 1-6 years. Clin Ophthalmol 2018;12:1533-7.  Back to cited text no. 7
    
8.
Amitava AK, Kewlani D, Khan Z, Razzak A. Assessment of a modification of Brückner's test as a screening modality for anisometropia and strabismus. Oman J Ophthalmol 2011;4:131-5.  Back to cited text no. 8
    




 

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