Early detection of visual impairment in young children using a smartphone-based deep learning system

doi:10.1038/s41591-022-02180-9

. 2023 Feb;29(2):493-503.

doi: 10.1038/s41591-022-02180-9. Epub 2023 Jan 26.

Early detection of visual impairment in young children using a smartphone-based deep learning system

Wenben Chen^#¹, Ruiyang Li^#¹, Qinji Yu^#², Andi Xu^#¹, Yile Feng^#³, Ruixin Wang¹, Lanqin Zhao¹, Zhenzhe Lin¹, Yahan Yang¹, Duoru Lin¹, Xiaohang Wu¹, Jingjing Chen¹, Zhenzhen Liu¹, Yuxuan Wu¹, Kang Dang³, Kexin Qiu³, Zilong Wang³, Ziheng Zhou³, Dong Liu¹, Qianni Wu¹, Mingyuan Li¹, Yifan Xiang¹, Xiaoyan Li¹, Zhuoling Lin¹, Danqi Zeng¹, Yunjian Huang¹, Silang Mo⁴, Xiucheng Huang⁴, Shulin Sun⁵, Jianmin Hu⁶, Jun Zhao⁷, Meirong Wei⁸, Shoulong Hu^{9

10}, Liang Chen¹¹, Bingfa Dai⁶, Huasheng Yang¹, Danping Huang¹, Xiaoming Lin¹, Lingyi Liang¹, Xiaoyan Ding¹, Yangfan Yang¹, Pengsen Wu¹, Feihui Zheng¹², Nick Stanojcic¹³, Ji-Peng Olivia Li¹⁴, Carol Y Cheung¹⁵, Erping Long¹, Chuan Chen¹⁶, Yi Zhu¹⁷, Patrick Yu-Wai-Man^{14

18

19

20}, Ruixuan Wang²¹, Wei-Shi Zheng²¹, Xiaowei Ding^{22

23}, Haotian Lin^{24

25

26}

Affiliations

¹ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
² Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China.
³ VoxelCloud, Shanghai, China.
⁴ School of Medicine, Sun Yat-sen University, Shenzhen, China.
⁵ Department of Urology, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.
⁶ Department of Ophthalmology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
⁷ Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China.
⁸ Liuzhou Maternity and Child Healthcare Hospital, Affiliated Women and Children's Hospital of Guangxi University of Science and Technology, Liuzhou, China.
⁹ National Center for Children's Health, Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, Beijing, China.
¹⁰ Department of Ophthalmology, Zhengzhou Children's Hospital, Zhengzhou, China.
¹¹ Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
¹² Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
¹³ Department of Ophthalmology, St. Thomas' Hospital, London, UK.
¹⁴ Moorfields Eye Hospital, London, UK.
¹⁵ Department of Ophthalmology & Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China.
¹⁶ Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
¹⁷ Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA.
¹⁸ University College London Institute of Ophthalmology, University College London, London, UK.
¹⁹ Cambridge Eye Unit, Addenbrooke's Hospital, Cambridge University Hospitals, Cambridge, UK.
²⁰ Cambridge Center for Brain Repair and Medical Research Council (MRC) Mitochondrial Biology Unit, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
²¹ School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
²² Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China. dingxiaowei@sjtu.edu.cn.
²³ VoxelCloud, Shanghai, China. dingxiaowei@sjtu.edu.cn.
²⁴ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China. linht5@mail.sysu.edu.cn.
²⁵ Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, China. linht5@mail.sysu.edu.cn.
²⁶ Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China. linht5@mail.sysu.edu.cn.

^# Contributed equally.

PMID: 36702948
DOI: 10.1038/s41591-022-02180-9

Early detection of visual impairment in young children using a smartphone-based deep learning system

Wenben Chen et al. Nat Med. 2023 Feb.

. 2023 Feb;29(2):493-503.

doi: 10.1038/s41591-022-02180-9. Epub 2023 Jan 26.

Authors

Affiliations

¹ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
² Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China.
³ VoxelCloud, Shanghai, China.
⁴ School of Medicine, Sun Yat-sen University, Shenzhen, China.
⁵ Department of Urology, Peking University Third Hospital, Peking University Health Science Center, Beijing, China.
⁶ Department of Ophthalmology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
⁷ Shenzhen People's Hospital (The Second Clinical Medical College, Jinan University; The First Affiliated Hospital, Southern University of Science and Technology), Shenzhen, China.
⁸ Liuzhou Maternity and Child Healthcare Hospital, Affiliated Women and Children's Hospital of Guangxi University of Science and Technology, Liuzhou, China.
⁹ National Center for Children's Health, Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, Beijing, China.
¹⁰ Department of Ophthalmology, Zhengzhou Children's Hospital, Zhengzhou, China.
¹¹ Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China.
¹² Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
¹³ Department of Ophthalmology, St. Thomas' Hospital, London, UK.
¹⁴ Moorfields Eye Hospital, London, UK.
¹⁵ Department of Ophthalmology & Visual Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China.
¹⁶ Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL, USA.
¹⁷ Department of Molecular and Cellular Pharmacology, University of Miami Miller School of Medicine, Miami, FL, USA.
¹⁸ University College London Institute of Ophthalmology, University College London, London, UK.
¹⁹ Cambridge Eye Unit, Addenbrooke's Hospital, Cambridge University Hospitals, Cambridge, UK.
²⁰ Cambridge Center for Brain Repair and Medical Research Council (MRC) Mitochondrial Biology Unit, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
²¹ School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China.
²² Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China. dingxiaowei@sjtu.edu.cn.
²³ VoxelCloud, Shanghai, China. dingxiaowei@sjtu.edu.cn.
²⁴ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China. linht5@mail.sysu.edu.cn.
²⁵ Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou, China. linht5@mail.sysu.edu.cn.
²⁶ Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China. linht5@mail.sysu.edu.cn.

^# Contributed equally.

PMID: 36702948
DOI: 10.1038/s41591-022-02180-9

Abstract

Early detection of visual impairment is crucial but is frequently missed in young children, who are capable of only limited cooperation with standard vision tests. Although certain features of visually impaired children, such as facial appearance and ocular movements, can assist ophthalmic practice, applying these features to real-world screening remains challenging. Here, we present a mobile health (mHealth) system, the smartphone-based Apollo Infant Sight (AIS), which identifies visually impaired children with any of 16 ophthalmic disorders by recording and analyzing their gazing behaviors and facial features under visual stimuli. Videos from 3,652 children (≤48 months in age; 54.5% boys) were prospectively collected to develop and validate this system. For detecting visual impairment, AIS achieved an area under the receiver operating curve (AUC) of 0.940 in an internal validation set and an AUC of 0.843 in an external validation set collected in multiple ophthalmology clinics across China. In a further test of AIS for at-home implementation by untrained parents or caregivers using their smartphones, the system was able to adapt to different testing conditions and achieved an AUC of 0.859. This mHealth system has the potential to be used by healthcare professionals, parents and caregivers for identifying young children with visual impairment across a wide range of ophthalmic disorders.

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References

1. Kliner, M., Fell, G., Pilling, R. & Bradbury, J. Visual impairment in children. Eye 25, 1097–1097 (2011). - PubMed - PMC - DOI
1. Mariotti, A. & Pascolini, D. Global estimates of visual impairment. Br. J. Ophthalmol. 96, 614–618 (2012). - PubMed - DOI
1. Bremond-Gignac, D., Copin, H., Lapillonne, A. & Milazzo, S. Visual development in infants: physiological and pathological mechanisms. Curr. Opin. Ophthalmol. 22, S1–S8 (2011). - PubMed - DOI
1. Teoh, L., Solebo, A. & Rahi, J. Temporal trends in the epidemiology of childhood severe visual impairment and blindness in the UK. Br. J. Ophthalmol. https://doi.org/10.1136/bjophthalmol-2021-320119 (2021). - DOI - PubMed
1. Gothwal, V. K., Lovie-Kitchin, J. E. & Nutheti, R. The development of the LV Prasad-Functional Vision Questionnaire: a measure of functional vision performance of visually impaired children. Investigative Ophthalmol. Vis. Sci. 44, 4131–4139 (2003). - DOI

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[1] Kliner, M., Fell, G., Pilling, R. & Bradbury, J. Visual impairment in children. Eye 25, 1097–1097 (2011). - PubMed - PMC - DOI

[2] Kliner, M., Fell, G., Pilling, R. & Bradbury, J. Visual impairment in children. Eye 25, 1097–1097 (2011). - PubMed - PMC - DOI

[3] Mariotti, A. & Pascolini, D. Global estimates of visual impairment. Br. J. Ophthalmol. 96, 614–618 (2012). - PubMed - DOI

[4] Mariotti, A. & Pascolini, D. Global estimates of visual impairment. Br. J. Ophthalmol. 96, 614–618 (2012). - PubMed - DOI

[5] Bremond-Gignac, D., Copin, H., Lapillonne, A. & Milazzo, S. Visual development in infants: physiological and pathological mechanisms. Curr. Opin. Ophthalmol. 22, S1–S8 (2011). - PubMed - DOI

[6] Bremond-Gignac, D., Copin, H., Lapillonne, A. & Milazzo, S. Visual development in infants: physiological and pathological mechanisms. Curr. Opin. Ophthalmol. 22, S1–S8 (2011). - PubMed - DOI

[7] Teoh, L., Solebo, A. & Rahi, J. Temporal trends in the epidemiology of childhood severe visual impairment and blindness in the UK. Br. J. Ophthalmol. https://doi.org/10.1136/bjophthalmol-2021-320119 (2021). - DOI - PubMed

[8] Teoh, L., Solebo, A. & Rahi, J. Temporal trends in the epidemiology of childhood severe visual impairment and blindness in the UK. Br. J. Ophthalmol. https://doi.org/10.1136/bjophthalmol-2021-320119 (2021). - DOI - PubMed

[9] Gothwal, V. K., Lovie-Kitchin, J. E. & Nutheti, R. The development of the LV Prasad-Functional Vision Questionnaire: a measure of functional vision performance of visually impaired children. Investigative Ophthalmol. Vis. Sci. 44, 4131–4139 (2003). - DOI

[10] Gothwal, V. K., Lovie-Kitchin, J. E. & Nutheti, R. The development of the LV Prasad-Functional Vision Questionnaire: a measure of functional vision performance of visually impaired children. Investigative Ophthalmol. Vis. Sci. 44, 4131–4139 (2003). - DOI

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Early detection of visual impairment in young children using a smartphone-based deep learning system

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Early detection of visual impairment in young children using a smartphone-based deep learning system

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