Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence.
<h4>Purpose</h4>Abnormalities of the running pattern of choroidal vessel have been reported in eyes with pachychoroid diseases. However, it is difficult for clinicians to judge the running pattern with high reproducibility. Thus, the purpose of this study was to compare the degree of con...
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2021
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oai:doaj.org-article:ee2d14283ded47ebb369be93e205f0312021-12-02T20:04:01ZArtificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence.1932-620310.1371/journal.pone.0251553https://doaj.org/article/ee2d14283ded47ebb369be93e205f0312021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251553https://doaj.org/toc/1932-6203<h4>Purpose</h4>Abnormalities of the running pattern of choroidal vessel have been reported in eyes with pachychoroid diseases. However, it is difficult for clinicians to judge the running pattern with high reproducibility. Thus, the purpose of this study was to compare the degree of concordance of the running pattern of the choroidal vessels between that determined by artificial intelligence (AI) to that determined by experienced clinicians.<h4>Methods</h4>The running pattern of the choroidal vessels in en face images of Haller's layer of 413 normal and pachychoroid diseased eyes was classified as symmetrical or asymmetrical by human raters and by three supervised machine learning models; the support vector machine (SVM), Xception, and random forest models. The data from the human raters were used as the supervised data. The accuracy rates of the human raters and the certainty of AI's answers were compared using confidence scores (CSs).<h4>Results</h4>The choroidal vascular running pattern could be determined by each AI model with an area under the curve better than 0.94. The random forest method was able to discriminate with the highest accuracy among the three AIs. In the CS analyses, the percentage of certainty was highest (66.4%) and that of uncertainty was lowest (6.1%) in the agreement group. On the other hand, the rate of uncertainty was highest (27.3%) in the disagreement group.<h4>Conclusion</h4>AI algorithm can automatically classify with ambiguous criteria the presence or absence of a symmetrical blood vessel running pattern of the choroid. The classification was as good as that of supervised humans in accuracy and reproducibility.Shozo SonodaHideki ShiiharaHiroto TerasakiNaoko KakiuchiRyoh FunatsuMasatoshi TomitaYuki ShinoharaEisuke UchinoTakuma UdagawaGuangzhou AnMasahiro AkibaHideo YokotaTaiji SakamotoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251553 (2021) |
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DOAJ |
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| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Shozo Sonoda Hideki Shiihara Hiroto Terasaki Naoko Kakiuchi Ryoh Funatsu Masatoshi Tomita Yuki Shinohara Eisuke Uchino Takuma Udagawa Guangzhou An Masahiro Akiba Hideo Yokota Taiji Sakamoto Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence. |
| description |
<h4>Purpose</h4>Abnormalities of the running pattern of choroidal vessel have been reported in eyes with pachychoroid diseases. However, it is difficult for clinicians to judge the running pattern with high reproducibility. Thus, the purpose of this study was to compare the degree of concordance of the running pattern of the choroidal vessels between that determined by artificial intelligence (AI) to that determined by experienced clinicians.<h4>Methods</h4>The running pattern of the choroidal vessels in en face images of Haller's layer of 413 normal and pachychoroid diseased eyes was classified as symmetrical or asymmetrical by human raters and by three supervised machine learning models; the support vector machine (SVM), Xception, and random forest models. The data from the human raters were used as the supervised data. The accuracy rates of the human raters and the certainty of AI's answers were compared using confidence scores (CSs).<h4>Results</h4>The choroidal vascular running pattern could be determined by each AI model with an area under the curve better than 0.94. The random forest method was able to discriminate with the highest accuracy among the three AIs. In the CS analyses, the percentage of certainty was highest (66.4%) and that of uncertainty was lowest (6.1%) in the agreement group. On the other hand, the rate of uncertainty was highest (27.3%) in the disagreement group.<h4>Conclusion</h4>AI algorithm can automatically classify with ambiguous criteria the presence or absence of a symmetrical blood vessel running pattern of the choroid. The classification was as good as that of supervised humans in accuracy and reproducibility. |
| format |
article |
| author |
Shozo Sonoda Hideki Shiihara Hiroto Terasaki Naoko Kakiuchi Ryoh Funatsu Masatoshi Tomita Yuki Shinohara Eisuke Uchino Takuma Udagawa Guangzhou An Masahiro Akiba Hideo Yokota Taiji Sakamoto |
| author_facet |
Shozo Sonoda Hideki Shiihara Hiroto Terasaki Naoko Kakiuchi Ryoh Funatsu Masatoshi Tomita Yuki Shinohara Eisuke Uchino Takuma Udagawa Guangzhou An Masahiro Akiba Hideo Yokota Taiji Sakamoto |
| author_sort |
Shozo Sonoda |
| title |
Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence. |
| title_short |
Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence. |
| title_full |
Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence. |
| title_fullStr |
Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence. |
| title_full_unstemmed |
Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence. |
| title_sort |
artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2021 |
| url |
https://doaj.org/article/ee2d14283ded47ebb369be93e205f031 |
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