Validation of an Artificial Intelligence Tool for the Detection of Sperm DNA Fragmentation Using the TUNEL In Situ Hybridization Assay

• URL: http://arxiv.org/abs/2510.11142v1
• Date: Mon, 13 Oct 2025 08:32:11 GMT
• Title: Validation of an Artificial Intelligence Tool for the Detection of Sperm DNA Fragmentation Using the TUNEL In Situ Hybridization Assay
• Abstract summary: Sperm DNA fragmentation (SDF) is a critical parameter in male fertility assessment that conventional semen analysis fails to evaluate.<n>This study presents the validation of a novel artificial intelligence (AI) tool designed to detect SDF through digital analysis of phase contrast microscopy images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sperm DNA fragmentation (SDF) is a critical parameter in male fertility assessment that conventional semen analysis fails to evaluate. This study presents the validation of a novel artificial intelligence (AI) tool designed to detect SDF through digital analysis of phase contrast microscopy images, using the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay as the gold standard reference. Utilising the established link between sperm morphology and DNA integrity, the present work proposes a morphology assisted ensemble AI model that combines image processing techniques with state-of-the-art transformer based machine learning models (GC-ViT) for the prediction of DNA fragmentation in sperm from phase contrast images. The ensemble model is benchmarked against a pure transformer `vision' model as well as a `morphology-only` model. Promising results show the proposed framework is able to achieve sensitivity of 60\% and specificity of 75\%. This non-destructive methodology represents a significant advancement in reproductive medicine by enabling real-time sperm selection based on DNA integrity for clinical diagnostic and therapeutic applications.

Related papers

• Towards Reliable Stain Transfer: An Iterative Data-Model Co-Optimization Framework Based on Multimodal Expert-Guided Assessment [55.16957949238588]
  We propose DMCoStain, a novel Data-Model Co-optimization framework for Stain transfer.<n>It iteratively co-refines training data and model capability, improving staining accuracy and interpretability in both pathological and structural consistency.<n>To support MEGFS, we construct ImmunoInstruction, the first large-scale IPE instruction-following dataset with 150K VQA samples.
  arXiv  Detail & Related papers  (2026-07-28T07:54:43Z)
• Dementia Etiology Diagnosis via Collaborative Meta Knowledge Enhancement [81.27196033392559]
  We propose a Collaborative Meta Knowledge Enhancement (COME) framework for dementia etiology diagnosis.<n>COME injects multi-center acquisition semantics, source identifiers, and modality indicators as heterogeneity-aware embeddings into a unified Transformer architecture for scale-up training.<n>Our method achieves state-of-the-art in-domain performance with a mean macro-averaged AUC of 85.62% and a 4.29-point gain over the strongest baseline.
  arXiv  Detail & Related papers  (2026-07-24T04:26:47Z)
• Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology [47.79180201495836]
  MixTIME is a multimodal foundation model that integrates pathology foundation models trained across distinct modalities.<n>It predicts multiplex immunofluorescence (mIF) protein expression from hematoxylin and eosin whole-slide images.<n>MixTIME achieves state-of-the-art performance across 17 protein markers as measured by correlation metrics.
  arXiv  Detail & Related papers  (2026-06-16T16:22:42Z)
• Artefact-Aware Fungal Detection in Dermatophytosis: A Real-Time Transformer-Based Approach for KOH Microscopy [0.28909295536379814]
  This study presents a transformer-based detection framework using the RT-DETR model architecture.<n>It achieves precise, query-driven localization of fungal structures in high-resolution potassium hydroxide (KOH) images.
  arXiv  Detail & Related papers  (2026-02-22T12:35:17Z)
• Data Augmentation for High-Fidelity Generation of CAR-T/NK Immunological Synapse Images [40.26649380711007]
  Chimeric antigen receptor (CAR)-T and NK cell immunotherapies have transformed cancer treatment.<n>The quality of the CAR-T/NK cell immunological synapse (IS) may serve as a functional biomarker for predicting therapeutic efficacy.
  arXiv  Detail & Related papers  (2026-02-01T00:54:53Z)
• A Semantically Enhanced Generative Foundation Model Improves Pathological Image Synthesis [82.01597026329158]
  We introduce a Correlation-Regulated Alignment Framework for Tissue Synthesis (CRAFTS) for pathology-specific text-to-image synthesis.<n>CRAFTS incorporates a novel alignment mechanism that suppresses semantic drift to ensure biological accuracy.<n>This model generates diverse pathological images spanning 30 cancer types, with quality rigorously validated by objective metrics and pathologist evaluations.
  arXiv  Detail & Related papers  (2025-12-15T10:22:43Z)
• Knowledge-based anomaly detection for identifying network-induced shape artifacts [3.29352273631268]
  This work introduces a novel knowledge-based anomaly detection method for detecting network-induced shape artifacts in synthetic images.<n>We demonstrate the effectiveness of the method for identifying network-induced shape artifacts in two synthetic mammography datasets.
  arXiv  Detail & Related papers  (2025-11-06T18:19:49Z)
• A Hybrid Framework Bridging CNN and ViT based on Theory of Evidence for Diabetic Retinopathy Grading [31.032002367721947]
  Diabetic retinopathy (DR) is a leading cause of vision loss among middle-aged and elderly people.<n>A variety of automated DR diagnosis systems have been recently established based on convolutional neural network (CNN) or vision Transformer (ViT)
  arXiv  Detail & Related papers  (2025-10-30T10:08:06Z)
• Beyond Imaging: Vision Transformer Digital Twin Surrogates for 3D+T Biological Tissue Dynamics [0.433313790597694]
  Vision Transformer Digital Twin Surrogate Network (VT-DTSN) is a deep learning framework for predictive modeling of 3D+T imaging data from biological tissue.<n>VT-DTSN learns to reconstruct high-fidelity, time-resolved dynamics of a Drosophila midgut.
  arXiv  Detail & Related papers  (2025-08-21T16:24:24Z)
• Object Detection for Medical Image Analysis: Insights from the RT-DETR Model [40.593685087097995]
  This paper focuses on the application of a novel detection framework based on the RT-DETR model for analyzing intricate image data.<n>The proposed RT-DETR model, built on a Transformer-based architecture, excels at processing high-dimensional and complex visual data with enhanced robustness and accuracy.
  arXiv  Detail & Related papers  (2025-01-27T20:02:53Z)
• Merging synthetic and real embryo data for advanced AI predictions [69.07284335967019]
  We train two generative models using two datasets-one we created and made publicly available, and one existing public dataset-to generate synthetic embryo images at various cell stages.<n>These were combined with real images to train classification models for embryo cell stage prediction.<n>Our results demonstrate that incorporating synthetic images alongside real data improved classification performance, with the model achieving 97% accuracy compared to 94.5% when trained solely on real data.
  arXiv  Detail & Related papers  (2024-12-02T08:24:49Z)
• Interpretable cancer cell detection with phonon microscopy using multi-task conditional neural networks for inter-batch calibration [39.759100498329275]
  We present a conditional neural network framework to simultaneously achieve inter-batch calibration. We validate our approach by training and validating on different experimental batches. We extend our model to reconstruct denoised signals, enabling physical interpretation of salient features indicating disease state.
  arXiv  Detail & Related papers  (2024-03-26T12:20:10Z)
• Brain Imaging-to-Graph Generation using Adversarial Hierarchical Diffusion Models for MCI Causality Analysis [44.45598796591008]
  Brain imaging-to-graph generation (BIGG) framework is proposed to map functional magnetic resonance imaging (fMRI) into effective connectivity for mild cognitive impairment analysis. The hierarchical transformers in the generator are designed to estimate the noise at multiple scales. Evaluations of the ADNI dataset demonstrate the feasibility and efficacy of the proposed model.
  arXiv  Detail & Related papers  (2023-05-18T06:54:56Z)
• Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion [3.290987481767681]
  We propose an image synthesis mechanism for multi-sequence prostate MR images conditioned on text. We generate paired bi-parametric images conditioned on images conditioned on paired data. We validate our method using 2D image slices from real suspected prostate cancer patients.
  arXiv  Detail & Related papers  (2023-03-03T17:24:39Z)
• Improving Mitosis Detection Via UNet-based Adversarial Domain Homogenizer [1.7298084639157258]
  This paper proposes a domain homogenizer for mitosis detection that attempts to alleviate domain differences in histology images via adversarial reconstruction of input images. We demonstrate our domain homogenizer's effectiveness by observing the reduction in domain differences between the preprocessed images. Using this homogenizer, along with a subsequent retina-net object detector, we were able to outperform the baselines of the 2021 MIDOG challenge in terms of average precision of the detected mitotic figures.
  arXiv  Detail & Related papers  (2022-09-15T11:15:57Z)
• From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning [40.83037811977803]
  Dynaformer is a graph-based deep learning model developed to predict protein-ligand binding affinities. It exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset. In a virtual screening on heat shock protein 90 (HSP90), 20 candidates are identified and their binding affinities are experimentally validated.
  arXiv  Detail & Related papers  (2022-08-19T14:55:12Z)
• Texture Characterization of Histopathologic Images Using Ecological Diversity Measures and Discrete Wavelet Transform [82.53597363161228]
  This paper proposes a method for characterizing texture across histopathologic images with a considerable success rate. It is possible to quantify the intrinsic properties of such images with promising accuracy on two HI datasets.
  arXiv  Detail & Related papers  (2022-02-27T02:19:09Z)
• Deep learning-based bias transfer for overcoming laboratory differences of microscopic images [0.0]
  We evaluate, compare, and improve existing generative model architectures to overcome domain shifts for immunofluorescence (IF) and Hematoxylin and Eosin (H&E) stained microscopy images. Adapting the bias of the samples significantly improved the pixel-level segmentation for human kidney glomeruli and podocytes and improved the classification accuracy for human prostate biopsies by up to 14%.
  arXiv  Detail & Related papers  (2021-05-25T09:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.