Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors
Positive diagnoses of gastric tumors from photodynamic diagnosis (PDD) images after the administration of 5-aminolevulinic acid are subjectively identified by expert endoscopists. Objective methods of tumor identification are needed to reduce potential misidentifications. We developed two methods to...
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MDPI AG
2022-05-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/11/11/3030 |
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| author | Taro Yamashita Hiroki Kurumi Masashi Fujii Takuki Sakaguchi Takeshi Hashimoto Hidehito Kinoshita Tsutomu Kanda Takumi Onoyama Yuichiro Ikebuchi Akira Yoshida Koichiro Kawaguchi Kazuo Yashima Hajime Isomoto |
| author_facet | Taro Yamashita Hiroki Kurumi Masashi Fujii Takuki Sakaguchi Takeshi Hashimoto Hidehito Kinoshita Tsutomu Kanda Takumi Onoyama Yuichiro Ikebuchi Akira Yoshida Koichiro Kawaguchi Kazuo Yashima Hajime Isomoto |
| author_sort | Taro Yamashita |
| collection | DOAJ |
| description | Positive diagnoses of gastric tumors from photodynamic diagnosis (PDD) images after the administration of 5-aminolevulinic acid are subjectively identified by expert endoscopists. Objective methods of tumor identification are needed to reduce potential misidentifications. We developed two methods to identify gastric tumors from PDD images. Method one was applied to segmented regions in the PDD endoscopic image to determine the region in LAB color space to be attributed to tumors using a multi-layer neural network. Method two aimed to diagnose tumors and determine regions in the PDD endoscopic image attributed to tumors using the convoluted neural network method. The efficiencies of diagnosing tumors were 77.8% (7/9) and 93.3% (14/15) for method one and method two, respectively. The efficiencies of determining tumor region defined as the ratio of the area were 35.7% (0.0–78.0) and 48.5% (3.0–89.1) for method one and method two, respectively. False-positive rates defined as the ratio of the area were 0.3% (0.0–2.0) and 3.8% (0.0–17.4) for method one and method two, respectively. Objective methods of determining tumor region in 5-aminolevulinic acid-based endoscopic PDD were developed by identifying regions in LAB color space attributed to tumors or by applying a method of convoluted neural network. |
| first_indexed | 2024-03-10T01:11:52Z |
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| id | doaj.art-0db75a4c89ba4cd5807a3d4176c70dcd |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-10T01:11:52Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-0db75a4c89ba4cd5807a3d4176c70dcd2023-11-23T14:15:37ZengMDPI AGJournal of Clinical Medicine2077-03832022-05-011111303010.3390/jcm11113030Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric TumorsTaro Yamashita0Hiroki Kurumi1Masashi Fujii2Takuki Sakaguchi3Takeshi Hashimoto4Hidehito Kinoshita5Tsutomu Kanda6Takumi Onoyama7Yuichiro Ikebuchi8Akira Yoshida9Koichiro Kawaguchi10Kazuo Yashima11Hajime Isomoto12Division of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology, Sanin Rosai Hospital, 1-8-1, Kaike Shinden, Yonago 683-0002, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanDivision of Gastroenterology and Nephrology, Faculty of Medicine, Tottori University, 36-1, Nishi-cho, Yonago 683-8504, JapanPositive diagnoses of gastric tumors from photodynamic diagnosis (PDD) images after the administration of 5-aminolevulinic acid are subjectively identified by expert endoscopists. Objective methods of tumor identification are needed to reduce potential misidentifications. We developed two methods to identify gastric tumors from PDD images. Method one was applied to segmented regions in the PDD endoscopic image to determine the region in LAB color space to be attributed to tumors using a multi-layer neural network. Method two aimed to diagnose tumors and determine regions in the PDD endoscopic image attributed to tumors using the convoluted neural network method. The efficiencies of diagnosing tumors were 77.8% (7/9) and 93.3% (14/15) for method one and method two, respectively. The efficiencies of determining tumor region defined as the ratio of the area were 35.7% (0.0–78.0) and 48.5% (3.0–89.1) for method one and method two, respectively. False-positive rates defined as the ratio of the area were 0.3% (0.0–2.0) and 3.8% (0.0–17.4) for method one and method two, respectively. Objective methods of determining tumor region in 5-aminolevulinic acid-based endoscopic PDD were developed by identifying regions in LAB color space attributed to tumors or by applying a method of convoluted neural network.https://www.mdpi.com/2077-0383/11/11/3030photodynamic diagnosis5-aminolevulinic acidLAB color spaceneural network |
| spellingShingle | Taro Yamashita Hiroki Kurumi Masashi Fujii Takuki Sakaguchi Takeshi Hashimoto Hidehito Kinoshita Tsutomu Kanda Takumi Onoyama Yuichiro Ikebuchi Akira Yoshida Koichiro Kawaguchi Kazuo Yashima Hajime Isomoto Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors Journal of Clinical Medicine photodynamic diagnosis 5-aminolevulinic acid LAB color space neural network |
| title | Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
| title_full | Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
| title_fullStr | Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
| title_full_unstemmed | Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
| title_short | Objective Methods of 5-Aminolevulinic Acid-Based Endoscopic Photodynamic Diagnosis Using Artificial Intelligence for Identification of Gastric Tumors |
| title_sort | objective methods of 5 aminolevulinic acid based endoscopic photodynamic diagnosis using artificial intelligence for identification of gastric tumors |
| topic | photodynamic diagnosis 5-aminolevulinic acid LAB color space neural network |
| url | https://www.mdpi.com/2077-0383/11/11/3030 |
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