Detection of sperm cells by single-stage and two-stage deep object detectors

Yuzkat, Mecit; İlhan, Hamza Osman; Aydin, Nizamettin

doi:10.1016/j.bspc.2023.104630

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…Therefore, to answer the PICO questions formulated in the previous section, we organized and schematically summarized them in the upcoming tables. Indeed, the studies were divided based on the general topic they dealt with; therefore, the specific variables considered in each model included sperm retrieval (Table 2, four studies [37][38][39][40]); sperm quality, further divided into the investigation of sperm quality and morphology (Table 3a, seventeen studies [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]) and quality of sperm and environmental factors (Table 3b, four studies [58][59][60][61]); non-obstructive azoospermia (Table 4, three studies [62][63][64]); IVF outcome (Table 5, three studies [65][66][67]); environmental and medical factors (Table 6, twelve studies [68][69][70][71][72][73][74][75]…”

Section: Resultsmentioning

confidence: 99%

“…Abbreviation: BMI stands for body mass index; LH is luteinizing hormone; FSH is follicle-stimulating hormone; ICSI is intracytoplasmic sperm injection; TESE is testicular sperm extraction; T is testosterone; AUC is the area under the curve; NOA is non-obstructive azoospermia. We found that 86% of the included studies in this review used an ML algorithm to predict male infertility, while the remaining 14% were studies about the development of an ML algorithm [37][38][39] or for the purpose of predicting IVF outcome [20,42].…”

Section: Resultsmentioning

confidence: 99%

“…In these studies, about forty different AI and ML methods were used, most of the time in combination with other methods (mean = 3.1, IQR = 1-9). Two studies [37,43] reported the generic term ML, while in eight studies [42,43,[45][46][47]51,59,61], algorithms available on the industrial market were used. Of the 40 models analyzed, most are models with 73% supervised learning, 20% are unsupervised, 3% are semi-supervised, and the remaining 5% are models that can be programmed in both modes, as in the case of ANNs.…”

Section: Authorsmentioning

confidence: 99%

“…Figure2. Risk of Bias (RoB) analysis of retrospective and prospective studies performed using PROBAST[37][38][39][40][41][42][43][44]47,48,[54][55][56][57][58][59][60][61]63,64,71,72,[74][75][76][77]79].…”

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confidence: 99%

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Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Schmeis Arroyo,

Iosa,

Antonucci

et al. 2024

Healthcare

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Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Authorsmentioning

confidence: 99%

mentioning

confidence: 99%

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Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Schmeis Arroyo,

Iosa,

Antonucci

et al. 2024

Healthcare

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“…Translating a technique from technical development to clinical deployment can involve various factors that impact the reproducibility of the developed technique. 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.…”

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

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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.

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A novel approach for one-stage sperm detection using advanced multi-scale feature pyramid networks

Li,

Xia,

Han

et al. 2024

Biomedical Signal Processing and Control

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Detection of sperm cells by single-stage and two-stage deep object detectors

Cited by 5 publications

References 29 publications

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature

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

A novel approach for one-stage sperm detection using advanced multi-scale feature pyramid networks

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