Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies
Abstract Biomarkers for discrimination among different subgroups of idiopathic inflammatory myopathies (IIM) are difficult to identify and may involve multiple laboratory tests and time-consuming procedures. We assessed the potential for artificial intelligence (AI) to extract features such as densi...
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BMC
2025-01-01
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| Series: | Diagnostic Pathology |
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| Online Access: | https://doi.org/10.1186/s13000-025-01608-3 |
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| author | Alessandro Massaro Gerardo Cazzato Giuseppe Ingravallo Nadia Casatta Carmelo Lupo Angelo Vacca Florenzo Iannone Francesco Girolamo |
| author_facet | Alessandro Massaro Gerardo Cazzato Giuseppe Ingravallo Nadia Casatta Carmelo Lupo Angelo Vacca Florenzo Iannone Francesco Girolamo |
| author_sort | Alessandro Massaro |
| collection | DOAJ |
| description | Abstract Biomarkers for discrimination among different subgroups of idiopathic inflammatory myopathies (IIM) are difficult to identify and may involve multiple laboratory tests and time-consuming procedures. We assessed the potential for artificial intelligence (AI) to extract features such as density of endomysial microvessels based on automatic analysis of the CD31+ vascular network on muscle biopsy images. We also assessed the potential of this technique to save time and its agreement rate with analyses based on the manual selection of microvessels from the same images. A total of 84 images from 84 patients with IIM, diagnosed between 2014 and 2020, were retrieved and analyzed using the Fast Random Forest (FRF) technique. We built a lightweight and explainable algorithm for calculating the pixel percentage of CD31+ endomysial capillaries. The FRF technique applied on images of CD31-stained muscle sections achieved a good performance in the recognition of microvessels by estimating their density over a standard area corresponding to a sample of microscope image. The time spent for this analysis was 90% less than the manual choice of microvessels (estimated time considering the computational time and the time spent to manually detecting the microvessels features). The good performance of the FRF demonstrates that the CD31 pixel percentage of endomysial capillaries is sufficient for a correct estimation. Finally, the paper proposes a procedure to integrate AI in the pre-screening process. |
| format | Article |
| id | doaj-art-3e3e6ed0e4024a1cb448600285e490a3 |
| institution | Kabale University |
| issn | 1746-1596 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Diagnostic Pathology |
| spelling | doaj-art-3e3e6ed0e4024a1cb448600285e490a32025-02-02T12:06:18ZengBMCDiagnostic Pathology1746-15962025-01-0120111010.1186/s13000-025-01608-3Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathiesAlessandro Massaro0Gerardo Cazzato1Giuseppe Ingravallo2Nadia Casatta3Carmelo Lupo4Angelo Vacca5Florenzo Iannone6Francesco Girolamo7Department of Engineering, LUM University “Giuseppe Degennaro”Section of Molecular Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari “Aldo Moro”Section of Molecular Pathology, Department of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari “Aldo Moro”Diapath SpADiapath SpAGuido Baccelli Unit of Internal Medicine, Department of Precision and Regenerative Medicine and Jonian Area-(DiMePRe-J), University of Bari Aldo MoroSection of Rheumathology, Department of Precision Regenerative Medicine and Ionian Area (DiMePRe-J), University of Bari Aldo MoroUnit of Human Anatomy and Histology, Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of BariAbstract Biomarkers for discrimination among different subgroups of idiopathic inflammatory myopathies (IIM) are difficult to identify and may involve multiple laboratory tests and time-consuming procedures. We assessed the potential for artificial intelligence (AI) to extract features such as density of endomysial microvessels based on automatic analysis of the CD31+ vascular network on muscle biopsy images. We also assessed the potential of this technique to save time and its agreement rate with analyses based on the manual selection of microvessels from the same images. A total of 84 images from 84 patients with IIM, diagnosed between 2014 and 2020, were retrieved and analyzed using the Fast Random Forest (FRF) technique. We built a lightweight and explainable algorithm for calculating the pixel percentage of CD31+ endomysial capillaries. The FRF technique applied on images of CD31-stained muscle sections achieved a good performance in the recognition of microvessels by estimating their density over a standard area corresponding to a sample of microscope image. The time spent for this analysis was 90% less than the manual choice of microvessels (estimated time considering the computational time and the time spent to manually detecting the microvessels features). The good performance of the FRF demonstrates that the CD31 pixel percentage of endomysial capillaries is sufficient for a correct estimation. Finally, the paper proposes a procedure to integrate AI in the pre-screening process.https://doi.org/10.1186/s13000-025-01608-3FRF image processingMyositisNeovascularizationNecrotizing myopathy, CD31 |
| spellingShingle | Alessandro Massaro Gerardo Cazzato Giuseppe Ingravallo Nadia Casatta Carmelo Lupo Angelo Vacca Florenzo Iannone Francesco Girolamo Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies Diagnostic Pathology FRF image processing Myositis Neovascularization Necrotizing myopathy, CD31 |
| title | Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies |
| title_full | Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies |
| title_fullStr | Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies |
| title_full_unstemmed | Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies |
| title_short | Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies |
| title_sort | pre screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies |
| topic | FRF image processing Myositis Neovascularization Necrotizing myopathy, CD31 |
| url | https://doi.org/10.1186/s13000-025-01608-3 |
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