Artificial intelligence model for the assessment of unstained live sperm morphology
Traditional sperm morphology assessment requires staining and high magnification (100×), rendering sperm unsuitable for further use. We aimed to determine whether an in-house artificial intelligence (AI) model could reliably assess normal sperm morphology in living sperm and compare its performance...
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| Format: | Article |
| Language: | English |
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Bioscientifica
2025-05-01
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| Series: | Reproduction and Fertility |
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| Online Access: | https://raf.bioscientifica.com/view/journals/raf/6/2/RAF-25-0014.xml |
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| author | Jermphiphut Jaruenpunyasak Prawai Maneelert Marwan Nawae Chainarong Choksuchat |
| author_facet | Jermphiphut Jaruenpunyasak Prawai Maneelert Marwan Nawae Chainarong Choksuchat |
| author_sort | Jermphiphut Jaruenpunyasak |
| collection | DOAJ |
| description | Traditional sperm morphology assessment requires staining and high magnification (100×), rendering sperm unsuitable for further use. We aimed to determine whether an in-house artificial intelligence (AI) model could reliably assess normal sperm morphology in living sperm and compare its performance with that of computer-aided semen analysis and conventional semen analysis methods. In this experimental study, we enrolled 30 healthy male volunteers aged 18–40 years at the Songklanagarind Assisted Reproductive Centre, Songklanagarind Hospital. We developed a novel dataset of sperm morphological images captured with confocal laser scanning microscopy at low magnification and high resolution to train and validate an AI model. Semen samples were divided into three aliquots and assessed for unstained live sperm morphology using the AI model, whereas computer-aided and conventional semen analysis methods evaluated fixed sperm morphology. The performance of our in-house AI model for evaluating unstained live sperm morphology was compared with that of the other two methods. The in-house AI model showed the strongest correlation with computer-aided semen analysis (r = 0.88), followed by conventional semen analysis (r = 0.76). The correlation between computer-aided semen analysis and conventional semen analysis was weaker (r = 0.57). Both the in-house AI and conventional semen analysis methods detected normal sperm morphology at significantly higher rates than computer-aided semen analysis. The in-house AI model could enhance assisted reproductive technology outcomes by improving the selection of high-quality sperm with normal morphology. This could lead to better outcomes of intracytoplasmic sperm injections and other fertility treatments. |
| format | Article |
| id | doaj-art-89646efc5174406eb501d1040d3e977a |
| institution | Kabale University |
| issn | 2633-8386 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Bioscientifica |
| record_format | Article |
| series | Reproduction and Fertility |
| spelling | doaj-art-89646efc5174406eb501d1040d3e977a2025-08-20T03:52:57ZengBioscientificaReproduction and Fertility2633-83862025-05-016210.1530/RAF-25-00141Artificial intelligence model for the assessment of unstained live sperm morphologyJermphiphut Jaruenpunyasak0Prawai Maneelert1Marwan Nawae2Chainarong Choksuchat3Department of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla, ThailandDivision of Reproductive Medicine, Department of Obstetrics and Gynaecology, Faculty of Medicine, Prince of Songkla University, Songkhla, ThailandDepartment of Biomedical Sciences and Biomedical Engineering, Faculty of Medicine, Prince of Songkla University, Songkhla, ThailandDivision of Reproductive Medicine, Department of Obstetrics and Gynaecology, Faculty of Medicine, Prince of Songkla University, Songkhla, ThailandTraditional sperm morphology assessment requires staining and high magnification (100×), rendering sperm unsuitable for further use. We aimed to determine whether an in-house artificial intelligence (AI) model could reliably assess normal sperm morphology in living sperm and compare its performance with that of computer-aided semen analysis and conventional semen analysis methods. In this experimental study, we enrolled 30 healthy male volunteers aged 18–40 years at the Songklanagarind Assisted Reproductive Centre, Songklanagarind Hospital. We developed a novel dataset of sperm morphological images captured with confocal laser scanning microscopy at low magnification and high resolution to train and validate an AI model. Semen samples were divided into three aliquots and assessed for unstained live sperm morphology using the AI model, whereas computer-aided and conventional semen analysis methods evaluated fixed sperm morphology. The performance of our in-house AI model for evaluating unstained live sperm morphology was compared with that of the other two methods. The in-house AI model showed the strongest correlation with computer-aided semen analysis (r = 0.88), followed by conventional semen analysis (r = 0.76). The correlation between computer-aided semen analysis and conventional semen analysis was weaker (r = 0.57). Both the in-house AI and conventional semen analysis methods detected normal sperm morphology at significantly higher rates than computer-aided semen analysis. The in-house AI model could enhance assisted reproductive technology outcomes by improving the selection of high-quality sperm with normal morphology. This could lead to better outcomes of intracytoplasmic sperm injections and other fertility treatments.https://raf.bioscientifica.com/view/journals/raf/6/2/RAF-25-0014.xmlassisted reproductive technologyartificial intelligenceconfocal microscopysemen analysissperm morphology |
| spellingShingle | Jermphiphut Jaruenpunyasak Prawai Maneelert Marwan Nawae Chainarong Choksuchat Artificial intelligence model for the assessment of unstained live sperm morphology Reproduction and Fertility assisted reproductive technology artificial intelligence confocal microscopy semen analysis sperm morphology |
| title | Artificial intelligence model for the assessment of unstained live sperm morphology |
| title_full | Artificial intelligence model for the assessment of unstained live sperm morphology |
| title_fullStr | Artificial intelligence model for the assessment of unstained live sperm morphology |
| title_full_unstemmed | Artificial intelligence model for the assessment of unstained live sperm morphology |
| title_short | Artificial intelligence model for the assessment of unstained live sperm morphology |
| title_sort | artificial intelligence model for the assessment of unstained live sperm morphology |
| topic | assisted reproductive technology artificial intelligence confocal microscopy semen analysis sperm morphology |
| url | https://raf.bioscientifica.com/view/journals/raf/6/2/RAF-25-0014.xml |
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