A preliminary study of artificial intelligence to recognize tessellated fundus in visual function screening of 7-14 year old students.
Artificial intelligence
Tessellated fundus
Visual function screening
Journal
BMC ophthalmology
ISSN: 1471-2415
Titre abrégé: BMC Ophthalmol
Pays: England
ID NLM: 100967802
Informations de publication
Date de publication:
29 Oct 2024
29 Oct 2024
Historique:
received:
26
03
2024
accepted:
09
10
2024
medline:
30
10
2024
pubmed:
30
10
2024
entrez:
30
10
2024
Statut:
epublish
Résumé
To evaluate the accuracy of artificial intelligence (AI)-based technology in recognizing tessellated fundus in students aged 7-14 years. A retrospective study was conducted to collect consecutive fundus photographs for visual function screening of students aged 7-14 years old in Haikou City from June 2018 to May 2019, and 1907 cases were included in the study. Among them, 949 cases were male and 958cases were female. The results were manually analyzed by two attending ophthalmologists to ensure the accuracy of the results. In case of discrepancies between the results analyzed by the two methods, the manual results were used as the standard. To assess the sensitivity and specificity of AI in recognizing tessellated fundus, a Kappa consistency test was performed comparing the results of manual recognition with those of AI recognition. Among 1907 cases, 1782 cases, or 93.4%, were completely consistent with the recognition results of manual and AI; 125 cases, or 6.6%, were analyzed with differences. The diagnostic rates of manual and AI for tessellated fundus were 26.1% and 26.4%, respectively. The sensitivity, specificity and area of the ROC curve (AUC) of AI for recognizing tessellated fundus in students aged 7-14 years were 88.0%, 95.4% and 0.917, respectively. The results of test showed that that the manual and AI identification results were highly consistent (κ = 0.831, P = 0.000). AI analysis has high specificity and sensitivity for tessellated fundus identification in students aged 7-14 years, and it is feasible to apply artificial intelligence to visual function screening in students aged 7-14 years.
Sections du résumé
BACKGROUND
BACKGROUND
To evaluate the accuracy of artificial intelligence (AI)-based technology in recognizing tessellated fundus in students aged 7-14 years.
METHODS
METHODS
A retrospective study was conducted to collect consecutive fundus photographs for visual function screening of students aged 7-14 years old in Haikou City from June 2018 to May 2019, and 1907 cases were included in the study. Among them, 949 cases were male and 958cases were female. The results were manually analyzed by two attending ophthalmologists to ensure the accuracy of the results. In case of discrepancies between the results analyzed by the two methods, the manual results were used as the standard. To assess the sensitivity and specificity of AI in recognizing tessellated fundus, a Kappa consistency test was performed comparing the results of manual recognition with those of AI recognition.
RESULTS
RESULTS
Among 1907 cases, 1782 cases, or 93.4%, were completely consistent with the recognition results of manual and AI; 125 cases, or 6.6%, were analyzed with differences. The diagnostic rates of manual and AI for tessellated fundus were 26.1% and 26.4%, respectively. The sensitivity, specificity and area of the ROC curve (AUC) of AI for recognizing tessellated fundus in students aged 7-14 years were 88.0%, 95.4% and 0.917, respectively. The results of test showed that that the manual and AI identification results were highly consistent (κ = 0.831, P = 0.000).
CONCLUSION
CONCLUSIONS
AI analysis has high specificity and sensitivity for tessellated fundus identification in students aged 7-14 years, and it is feasible to apply artificial intelligence to visual function screening in students aged 7-14 years.
Identifiants
pubmed: 39472791
doi: 10.1186/s12886-024-03722-0
pii: 10.1186/s12886-024-03722-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
471Subventions
Organisme : Key R&D Plan Projects in Hainan Province
ID : ZDYF2020110
Informations de copyright
© 2024. The Author(s).
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