Early detection of visual impairment in young children using a smartphone-based deep learning system
© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:29 |
|---|---|
| Enthalten in: |
Nature medicine - 29(2023), 2 vom: 26. Feb., Seite 493-503 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Chen, Wenben [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
|---|
| Anmerkungen: |
Date Completed 24.02.2023 Date Revised 22.03.2023 published: Print-Electronic Citation Status MEDLINE |
|---|
| doi: |
10.1038/s41591-022-02180-9 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM352134283 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM352134283 | ||
| 003 | DE-627 | ||
| 005 | 20231226053024.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1038/s41591-022-02180-9 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1173.xml |
| 035 | |a (DE-627)NLM352134283 | ||
| 035 | |a (NLM)36702948 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Chen, Wenben |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Early detection of visual impairment in young children using a smartphone-based deep learning system |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 24.02.2023 | ||
| 500 | |a Date Revised 22.03.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023. The Author(s), under exclusive licence to Springer Nature America, Inc. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 700 | 1 | |a Li, Ruiyang |e verfasserin |4 aut | |
| 700 | 1 | |a Yu, Qinji |e verfasserin |4 aut | |
| 700 | 1 | |a Xu, Andi |e verfasserin |4 aut | |
| 700 | 1 | |a Feng, Yile |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Ruixin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Lanqin |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Zhenzhe |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yahan |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Duoru |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Xiaohang |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Jingjing |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Zhenzhen |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Yuxuan |e verfasserin |4 aut | |
| 700 | 1 | |a Dang, Kang |e verfasserin |4 aut | |
| 700 | 1 | |a Qiu, Kexin |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Zilong |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Ziheng |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Dong |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Qianni |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Mingyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Xiang, Yifan |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Xiaoyan |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Zhuoling |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Danqi |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Yunjian |e verfasserin |4 aut | |
| 700 | 1 | |a Mo, Silang |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Xiucheng |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Shulin |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Jianmin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Jun |e verfasserin |4 aut | |
| 700 | 1 | |a Wei, Meirong |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Shoulong |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Liang |e verfasserin |4 aut | |
| 700 | 1 | |a Dai, Bingfa |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Huasheng |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Danping |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Xiaoming |e verfasserin |4 aut | |
| 700 | 1 | |a Liang, Lingyi |e verfasserin |4 aut | |
| 700 | 1 | |a Ding, Xiaoyan |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yangfan |e verfasserin |4 aut | |
| 700 | 1 | |a Wu, Pengsen |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Feihui |e verfasserin |4 aut | |
| 700 | 1 | |a Stanojcic, Nick |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Ji-Peng Olivia |e verfasserin |4 aut | |
| 700 | 1 | |a Cheung, Carol Y |e verfasserin |4 aut | |
| 700 | 1 | |a Long, Erping |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Chuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Yi |e verfasserin |4 aut | |
| 700 | 1 | |a Yu-Wai-Man, Patrick |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Ruixuan |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Wei-Shi |e verfasserin |4 aut | |
| 700 | 1 | |a Ding, Xiaowei |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Haotian |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Nature medicine |d 1995 |g 29(2023), 2 vom: 26. Feb., Seite 493-503 |w (DE-627)NLM074659804 |x 1546-170X |7 nnns |
| 773 | 1 | 8 | |g volume:29 |g year:2023 |g number:2 |g day:26 |g month:02 |g pages:493-503 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1038/s41591-022-02180-9 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 29 |j 2023 |e 2 |b 26 |c 02 |h 493-503 | ||