A new deep‐learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure

Sato, Takuma; Kishi, Hiroshi; Murakata, Saori; Hayashi, Yasuhiro; Hattori, T.; Nakazawa, Shozo; Mori, Y.; Hidaka, Masaaki; Kasahara, Yuta; Kusuhara, Atsuko; Hosoya, Kayo; Hayashi, Hiroshi; Okamoto, Aikou

doi:10.1002/rmb2.12454

Cited by 10 publications

(3 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The YOLO v3 deep learning-based ML model detects abnormal sperm with a high sensitivity (0.881) and positive predictive value (0.853). In addition to morphological evaluation, this algorithm tracks the movement of sperm in a short time under an inverted microscope to select better-quality spermatozoa for ART procedures [40].…”

Section: Ai In Evaluation Of Sperm Morphologymentioning

confidence: 99%

Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis

Panner Selvam,

Moharana,

Baskaran

et al. 2024

Medicina

View full text Add to dashboard Cite

Background and Objectives: Infertility rates and the number of couples undergoing reproductive care have both increased substantially during the last few decades. Semen analysis is a crucial step in both the diagnosis and the treatment of male infertility. The accuracy of semen analysis results remains quite poor despite years of practice and advancements. Artificial intelligence (AI) algorithms, which can analyze and synthesize large amounts of data, can address the unique challenges involved in semen analysis due to the high objectivity of current methodologies. This review addresses recent AI advancements in semen analysis. Materials and Methods: A systematic literature search was performed in the PubMed database. Non-English articles and studies not related to humans were excluded. We extracted data related to AI algorithms or models used to evaluate semen parameters from the original studies, excluding abstracts, case reports, and meeting reports. Results: Of the 306 articles identified, 225 articles were rejected in the preliminary screening. The evaluation of the full texts of the remaining 81 publications resulted in the exclusion of another 48 articles, with a final inclusion of 33 original articles in this review. Conclusions: AI and machine learning are becoming increasingly popular in biomedical applications. The examination and selection of sperm by andrologists and embryologists may benefit greatly from using these algorithms. Furthermore, when bigger and more reliable datasets become accessible for training, these algorithms may improve over time.

show abstract

Section: Ai In Evaluation Of Sperm Morphologymentioning

confidence: 99%

Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis

Panner Selvam,

Moharana,

Baskaran

et al. 2024

Medicina

View full text Add to dashboard Cite

show abstract

“…The association task is primarily solved by explicitly or implicitly leveraging strong cues, including spatial and appearance information. Takuma, Sato [ 21 ], and colleagues employed YOLOv3 for object detection, followed by the SORT algorithm for object tracking. This approach allows for simultaneous morphological assessment and tracking, offering rapid evaluation speeds.…”

Section: Related Workmentioning

confidence: 99%

Sperm YOLOv8E-TrackEVD: A Novel Approach for Sperm Detection and Tracking

Zhang,

Chang

et al. 2024

Sensors

View full text Add to dashboard Cite

Male infertility is a global health issue, with 40–50% attributed to sperm abnormalities. The subjectivity and irreproducibility of existing detection methods pose challenges to sperm assessment, making the design of automated semen analysis algorithms crucial for enhancing the reliability of sperm evaluations. This paper proposes a comprehensive sperm tracking algorithm (Sperm YOLOv8E-TrackEVD) that combines an enhanced YOLOv8 small object detection algorithm (SpermYOLOv8-E) with an improved DeepOCSORT tracking algorithm (SpermTrack-EVD) to detect human sperm in a microscopic field of view and track healthy sperm in a sample in a short period effectively. Firstly, we trained the improved YOLOv8 model on the VISEM-Tracking dataset for accurate sperm detection. To enhance the detection of small sperm objects, we introduced an attention mechanism, added a small object detection layer, and integrated the SPDConv and Detect_DyHead modules. Furthermore, we used a new distance metric method and chose IoU loss calculation. Ultimately, we achieved a 1.3% increase in precision, a 1.4% increase in recall rate, and a 2.0% improvement in mAP@0.5:0.95. We applied SpermYOLOv8-E combined with SpermTrack-EVD for sperm tracking. On the VISEM-Tracking dataset, we achieved 74.303% HOTA and 71.167% MOTA. These results show the effectiveness of the designed Sperm YOLOv8E-TrackEVD approach in sperm tracking scenarios.

show abstract

“…Regardless of applications or the types of cells to analyze, the first technical step for deep learning models is often to visually identify and locate an object (oocyte [33,34], sperm [35][36][37][38][39], and embryo [20,[40][41][42]) in images. Different clinics, however, use different image acquisition conditions (e.g., microscope brands and models, imaging modes [43][44][45], magnifications [9,33], illumination intensity, and camera resolutions [13][14][15]39] etc. ), as evident in Table 1.…”

Section: Introductionmentioning

confidence: 99%

Testing the reproducibility and effectiveness of deep learning models among clinics: sperm detection as a pilot study

Wang,

Jin,

Jiang

et al. 2024

Preprint

View full text Add to dashboard Cite

Background: Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the reproducibility of object detection models, using sperm analysis as a pilot example. Methods: Ablation studies were performed using state-of-the-art models for detecting human sperm to quantitatively assess how model precision (false-positive detection) and recall (missed detection) were affected by imaging magnification, imaging mode, and sample preprocessing protocols. The results led to the hypothesis that the richness of image acquisition conditions in a training dataset deterministically affects model reproducibility. The hypothesis was tested by first enriching the training dataset with a wide range of imaging conditions, then validated through internal blind tests on new samples and external multi-center clinical validations. Results: Ablation experiments revealed that removing subsets of data from the training dataset significantly reduced model precision. Removing raw sample images from the training dataset caused the largest drop in model precision, whereas removing 20x images caused the largest drop in model recall. by incorporating different imaging and sample preprocessing conditions into a rich training dataset, the model achieved an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94-0.99) for precision, and an ICC of 0.97 (95% CI: 0.93-0.99) for recall. Multi-center clinical validation showed no significant differences in model precision or recall across different clinics and applications. Conclusions: The results validated the hypothesis that the richness of data in the training dataset is a key factor impacting model reproducibility. These findings highlight the importance of diversity in a training dataset for model evaluation and suggest that future deep learning models in andrology and reproductive medicine should incorporate comprehensive feature sets for enhanced reproducibility across clinics.

show abstract

A new deep‐learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure

Cited by 10 publications

References 24 publications

Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis

Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis

Sperm YOLOv8E-TrackEVD: A Novel Approach for Sperm Detection and Tracking

Testing the reproducibility and effectiveness of deep learning models among clinics: sperm detection as a pilot study

Contact Info

Product

Resources

About