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Abstract Study question Does AIOM-based live sperm morphology annotation provide consistent and accurate results for assessing sperm morphology? Summary answer AIOM offers precise and reproducible sperm morphology evaluations which can enhance diagnostic efficiency with a single and reliable test. What is known already Semen analysis remains the primary diagnostic tool for male infertility. Unlike concentration and motility, sperm morphology shows a much higher correlation with paternal genetic integrity. However, it poses challenges due to inter-observer and inter-laboratory variability. The 2023 semen proficiency test highlighted these inconsistencies, reporting deviations of up to 13% among laboratories and acceptability ranging from 0% to 43%. This inconsistency is linked to complex staining procedures and interpretive subjectivity. To address these challenges, our study evaluates a novel AIOM-assisted platform, employing a live sperm morphology deep learning model. We aim to explore the platform’s reproducibility and reliability. Study design, size, duration In the first stage, liquefied semen specimens were split into three aliquots, each assigned to one of three operators (operators 1–3). Each operator assessed sperm morphology using Diff-Quik and live sperm morphology using the LensHooke® X12 semen analysis system (Bonraybio, Taichung). In the second stage, a single well-trained operator conducted sperm morphology assessments for three replicates (replicates 1-3) using the two methods. Interclass-correlation coefficients (ICCs) were calculated to assess consistency between replicates and operators. Participants/materials, setting, methods Semen samples (n = 30) were collected post 2-5 day abstinence and liquefied at room temperature for 30 minutes. Specimens were diluted to 100 million/mL with HTF, and underwent Diff-Quik staining according to WHO’s instruction. Aliquot of 3 μL semen specimen was loaded to 10-μm LensHooke® CS3 slide and analyzed by X12 analysis using live sperm morphology algorithm. Main results and the role of chance X12 utilizes a deep learning algorithm to automatically annotate sperm morphology, focusing on parameters such as sperm head, midpiece, tail, and excess residual cytoplasm, ultimately generating outputs for normal morphology and the teratozoospermic index (TZI) based on WHO strict criteria. Compared to manual Diff-Quik analysis, X12 showed higher consistency across all parameters, with ICCs above 0.85, unlike the manual method’s lower ICCs (below 0.70). The determination of abnormal midpiece had the lowest agreement between operators ICC (95% CI): 0.15 (-0.61–0.58). In contrast, X12 demonstrated high agreement in all parameters, with ICC values consistently exceeding 0.85. When comparing the agreement of normal morphology rates between the two methods, a notable difference was observed (Manual method: 0.59 (0.22–0.8) versus X12: 0.8 (0.61–0.9). To minimize inter-operator variability, a highly trained operator conducted three replicates following the manual method, thereby reducing deviations between measures. Remarkably, a single assessment of live sperm morphology produced results that were comparable to the mean average of repeated measures obtained through manual methods. In conclusion, the new platform for sperm morphology assessment, contingent on AIOM technology, is a reproducible and reliable tool in male infertility diagnosis. Limitations, reasons for caution This study’s limitations include its laboratory-based nature and a small sample size. Further research should include a more diverse and larger patient cohort to validate the efficacy and accuracy of AIOM in live sperm morphology assessment. Wider implications of the findings AIOM’s integration of automated interpretation simplifies sperm morphology assessment, reducing sample requirements and is smear-free. This advancement not only enhances diagnostic testing consistency but also eases andrologists’ workloads. This platform can contribute to standardized sperm analysis and improved laboratory management. Trial registration number Not applicable
Chang et al. (Mon,) studied this question.