Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure Ulcers
Artificial intelligence (AI) in medical care can raise diagnosis accuracy and improve its uniformity. This study developed a diagnostic imaging system for chronic wounds that can be used in medically underpopulated areas. The image identification algorithm searches for patterns and makes decisions b...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/12/6/2194 |
| _version_ | 1797611030323396608 |
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| author | Shunsuke Sakakibara Akira Takekawa Chikara Takekawa Satoshi Nagai Hiroto Terashi |
| author_facet | Shunsuke Sakakibara Akira Takekawa Chikara Takekawa Satoshi Nagai Hiroto Terashi |
| author_sort | Shunsuke Sakakibara |
| collection | DOAJ |
| description | Artificial intelligence (AI) in medical care can raise diagnosis accuracy and improve its uniformity. This study developed a diagnostic imaging system for chronic wounds that can be used in medically underpopulated areas. The image identification algorithm searches for patterns and makes decisions based on information obtained from pixels rather than images. Images of 50 patients with pressure sores treated at Kobe University Hospital were examined. The algorithm determined the presence of necrosis with a significant difference (<i>p</i> = 3.39 × 10<sup>−5</sup>). A threshold value was created with a luminance difference of 50 for the group with necrosis of 5% or more black pixels. In the no-necrosis group with less than 5% black pixels, the threshold value was created with a brightness difference of 100. The “shallow wounds” were distributed below 100, whereas the “deep wounds” were distributed above 100. When the algorithm was applied to 24 images of 23 new cases, there was 100% agreement between the specialist and the algorithm regarding the presence of necrotic tissue and wound depth evaluation. The algorithm identifies the necrotic tissue and wound depth without requiring a large amount of data, making it suitable for application to future AI diagnosis systems for chronic wounds. |
| first_indexed | 2024-03-11T06:22:15Z |
| format | Article |
| id | doaj.art-9b01352a0bf8474786827b024af9bf1f |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-11T06:22:15Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-9b01352a0bf8474786827b024af9bf1f2023-11-17T11:49:25ZengMDPI AGJournal of Clinical Medicine2077-03832023-03-01126219410.3390/jcm12062194Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure UlcersShunsuke Sakakibara0Akira Takekawa1Chikara Takekawa2Satoshi Nagai3Hiroto Terashi4Department of Plastic Surgery, Kobe University Graduate School of Medicine, Kobe 650-0017, JapanDepartment of Plastic Surgery, Kobe University Graduate School of Medicine, Kobe 650-0017, JapanDepartment of Plastic Surgery, Kobe University Graduate School of Medicine, Kobe 650-0017, JapanGraduate School of Human Development and Environment, Kobe University, Kobe 657-8501, JapanDepartment of Plastic Surgery, Kobe University Graduate School of Medicine, Kobe 650-0017, JapanArtificial intelligence (AI) in medical care can raise diagnosis accuracy and improve its uniformity. This study developed a diagnostic imaging system for chronic wounds that can be used in medically underpopulated areas. The image identification algorithm searches for patterns and makes decisions based on information obtained from pixels rather than images. Images of 50 patients with pressure sores treated at Kobe University Hospital were examined. The algorithm determined the presence of necrosis with a significant difference (<i>p</i> = 3.39 × 10<sup>−5</sup>). A threshold value was created with a luminance difference of 50 for the group with necrosis of 5% or more black pixels. In the no-necrosis group with less than 5% black pixels, the threshold value was created with a brightness difference of 100. The “shallow wounds” were distributed below 100, whereas the “deep wounds” were distributed above 100. When the algorithm was applied to 24 images of 23 new cases, there was 100% agreement between the specialist and the algorithm regarding the presence of necrotic tissue and wound depth evaluation. The algorithm identifies the necrotic tissue and wound depth without requiring a large amount of data, making it suitable for application to future AI diagnosis systems for chronic wounds.https://www.mdpi.com/2077-0383/12/6/2194artificial intelligencedigital applicationwound caredecubitus ulcernecrotic tissue |
| spellingShingle | Shunsuke Sakakibara Akira Takekawa Chikara Takekawa Satoshi Nagai Hiroto Terashi Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure Ulcers Journal of Clinical Medicine artificial intelligence digital application wound care decubitus ulcer necrotic tissue |
| title | Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure Ulcers |
| title_full | Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure Ulcers |
| title_fullStr | Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure Ulcers |
| title_full_unstemmed | Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure Ulcers |
| title_short | Construction and Validation of an Image Discrimination Algorithm to Discriminate Necrosis from Wounds in Pressure Ulcers |
| title_sort | construction and validation of an image discrimination algorithm to discriminate necrosis from wounds in pressure ulcers |
| topic | artificial intelligence digital application wound care decubitus ulcer necrotic tissue |
| url | https://www.mdpi.com/2077-0383/12/6/2194 |
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