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Goss DM, Vasilescu SA, Vasilescu PA, Cooke S, Kim SH, Sacks GP, Gardner DK, Warkiani ME. Evaluation of an artificial intelligence-facilitated sperm detection tool in azoospermic samples for use in ICSI. Reprod Biomed Online 2024; 49:103910. [PMID: 38652944 DOI: 10.1016/j.rbmo.2024.103910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 01/31/2024] [Accepted: 02/09/2024] [Indexed: 04/25/2024]
Abstract
RESEARCH QUESTION Can artificial intelligence (AI) improve the efficiency and efficacy of sperm searches in azoospermic samples? DESIGN This two-phase proof-of-concept study began with a training phase using eight azoospermic patients (>10,000 sperm images) to provide a variety of surgically collected samples for sperm morphology and debris variation to train a convolutional neural network to identify spermatozoa. Second, side-by-side testing was undertaken on two cohorts of non-obstructive azoospermia patient samples: an embryologist versus the AI identifying all the spermatozoa in the still images (cohort 1, n = 4), and a side-by-side test with a simulated clinical deployment of the AI model with an intracytoplasmic sperm injection microscope and the embryologist performing a search with and without the aid of the AI (cohort 2, n = 4). RESULTS In cohort 1, the AI model showed an improvement in the time taken to identify all the spermatozoa per field of view (0.02 ± 0.30 × 10-5s versus 36.10 ± 1.18s, P < 0.0001) and improved recall (91.95 ± 0.81% versus 86.52 ± 1.34%, P < 0.001) compared with an embryologist. From a total of 2660 spermatozoa to find in all the samples combined, 1937 were found by an embryologist and 1997 were found by the AI in less than 1000th of the time. In cohort 2, the AI-aided embryologist took significantly less time per droplet (98.90 ± 3.19 s versus 168.7 ± 7.84 s, P < 0.0001) and found 1396 spermatozoa, while 1274 were found without AI, although no significant difference was observed. CONCLUSIONS AI-powered image analysis has the potential for seamless integration into laboratory workflows, to reduce the time to identify and isolate spermatozoa from surgical sperm samples from hours to minutes, thus increasing success rates from these treatments.
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Affiliation(s)
- Dale M Goss
- School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia.; IVFAustralia, Sydney, New South Wales, Australia
| | - Steven A Vasilescu
- School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia
| | | | - Simon Cooke
- IVFAustralia, Sydney, New South Wales, Australia
| | - Shannon Hk Kim
- IVFAustralia, Sydney, New South Wales, Australia.; University of New South Wales, Sydney, New South Wales, Australia
| | - Gavin P Sacks
- School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; IVFAustralia, Sydney, New South Wales, Australia.; University of New South Wales, Sydney, New South Wales, Australia
| | - David K Gardner
- NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia.; Melbourne IVF, Melbourne, Victoria, Australia
| | - Majid E Warkiani
- School of Biomedical Engineering, University of Technology Sydney, Sydney, New South Wales, Australia.; NeoGenix Biosciences Pty Ltd, Sydney, New South Wales, Australia.; Institute for Biomedical Materials & Devices (IBMD), University of Technology Sydney, Sydney, New South Wales, Australia..
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2
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García-Vázquez FA. Artificial intelligence and porcine breeding. Anim Reprod Sci 2024:107538. [PMID: 38926001 DOI: 10.1016/j.anireprosci.2024.107538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 06/13/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024]
Abstract
Livestock management is evolving into a new era, characterized by the analysis of vast quantities of data (Big Data) collected from both traditional breeding methods and new technologies such as sensors, automated monitoring system, and advanced analytics. Artificial intelligence (A-In), which refers to the capability of machines to mimic human intelligence, including subfields like machine learning and deep learning, is playing a pivotal role in this transformation. A wide array of A-In techniques, successfully employed in various industrial and scientific contexts, are now being integrated into mainstream livestock management practices. In the case of swine breeding, while traditional methods have yielded considerable success, the increasing amount of information requires the adoption of new technologies such as A-In to drive productivity, enhance animal welfare, and reduce environmental impact. Current findings suggest that these techniques have the potential to match or exceed the performance of traditional methods, often being more scalable in terms of efficiency and sustainability within the breeding industry. This review provides insights into the application of A-In in porcine breeding, from the perspectives of both sows (including welfare and reproductive management) and boars (including semen quality and health), and explores new approaches which are already being applied in other species.
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Affiliation(s)
- Francisco A García-Vázquez
- Departamento de Fisiología, Facultad de Veterinaria, Campus de Excelencia Mare Nostrum, Universidad de Murcia, Murcia 30100, Spain; Instituto Murciano de Investigación Biosanitaria (IMIB-Arrixaca), Murcia, Spain.
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3
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Fjeldstad J, Qi W, Mercuri N, Siddique N, Meriano J, Krivoi A, Nayot D. An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes. Reprod Biomed Online 2024; 48:103842. [PMID: 38552566 DOI: 10.1016/j.rbmo.2024.103842] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 01/04/2024] [Accepted: 01/10/2024] [Indexed: 06/11/2024]
Abstract
RESEARCH QUESTION Can a deep learning image analysis model be developed to assess oocyte quality by predicting blastocyst development from images of denuded mature oocytes? DESIGN A deep learning model was developed utilizing 37,133 static oocyte images with associated laboratory outcomes from eight fertility clinics (six countries). A subset of data (n = 7807) was allocated to test model performance. External model validation was conducted to assess generalizability and robustness on new data (n = 12,357) from two fertility clinics (two countries). Performance was assessed by calculating area under the curve (AUC), balanced accuracy, specificity and sensitivity. Subgroup analyses were performed on the test dataset for age group, male factor and geographical location of the clinic. Model probabilities of the external dataset were converted to a 0-10 scoring scale to facilitate analysis of correlation with blastocyst development and quality. RESULTS The deep learning model demonstrated AUC of 0.64, balanced accuracy of 0.60, specificity of 0.55 and sensitivity of 0.65 on the test dataset. Subgroup analyses displayed the highest performance for age group 38-39 years (AUC 0.68), a negligible impact of male factor, and good model generalizability across geographical locations. Model performance was confirmed on external data: AUC of 0.63, balanced accuracy of 0.58, specificity of 0.57 and sensitivity of 0.59. Analysis of the scoring scale revealed that higher scoring oocytes correlated with higher likelihood of blastocyst development and good-quality blastocyst formation. CONCLUSION The deep learning model showed a favourable performance for the evaluation of oocytes in terms of competence to develop into a blastocyst, and when the predictions were converted into scores, they correlated with blastocyst quality. This represents a significant first step in oocyte evaluation for scientific and clinical applications.
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Affiliation(s)
| | - Weikai Qi
- Future Fertility, Toronto, Ontario, Canada
| | | | | | | | | | - Dan Nayot
- Future Fertility, Toronto, Ontario, Canada
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4
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Rahman MA, Yan F, Li R, Wang Y, Huang L, Han R, Jiang Y. Deep Learning Insights into the Dynamic Effects of Photodynamic Therapy on Cancer Cells. Pharmaceutics 2024; 16:673. [PMID: 38794335 PMCID: PMC11125085 DOI: 10.3390/pharmaceutics16050673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/10/2024] [Accepted: 05/15/2024] [Indexed: 05/26/2024] Open
Abstract
Photodynamic therapy (PDT) shows promise in tumor treatment, particularly when combined with nanotechnology. This study examines the impact of deep learning, particularly the Cellpose algorithm, on the comprehension of cancer cell responses to PDT. The Cellpose algorithm enables robust morphological analysis of cancer cells, while logistic growth modelling predicts cellular behavior post-PDT. Rigorous model validation ensures the accuracy of the findings. Cellpose demonstrates significant morphological changes after PDT, affecting cellular proliferation and survival. The reliability of the findings is confirmed by model validation. This deep learning tool enhances our understanding of cancer cell dynamics after PDT. Advanced analytical techniques, such as morphological analysis and growth modeling, provide insights into the effects of PDT on hepatocellular carcinoma (HCC) cells, which could potentially improve cancer treatment efficacy. In summary, the research examines the role of deep learning in optimizing PDT parameters to personalize oncology treatment and improve efficacy.
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Affiliation(s)
- Md. Atiqur Rahman
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Feihong Yan
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ruiyuan Li
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yu Wang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lu Huang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Rongcheng Han
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yuqiang Jiang
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China; (M.A.R.); (F.Y.); (R.L.); (Y.W.); (L.H.)
- University of Chinese Academy of Sciences, Beijing 100049, China
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5
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Bouloorchi Tabalvandani M, Saeidpour Z, Habibi Z, Javadizadeh S, Firoozabadi SA, Badieirostami M. Microfluidics as an emerging paradigm for assisted reproductive technology: A sperm separation perspective. Biomed Microdevices 2024; 26:23. [PMID: 38652182 DOI: 10.1007/s10544-024-00705-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/08/2024] [Indexed: 04/25/2024]
Abstract
Millions of people are subject to infertility worldwide and one in every six people, regardless of gender, experiences infertility at some period in their life, according to the World Health Organization. Assisted reproductive technologies are defined as a set of procedures that can address the infertility issue among couples, culminating in the alleviation of the condition. However, the costly conventional procedures of assisted reproduction and the inherent vagaries of the processes involved represent a setback for its successful implementation. Microfluidics, an emerging tool for processing low-volume samples, have recently started to play a role in infertility diagnosis and treatment. Given its host of benefits, including manipulating cells at the microscale, repeatability, automation, and superior biocompatibility, microfluidics have been adopted for various procedures in assisted reproduction, ranging from sperm sorting and analysis to more advanced processes such as IVF-on-a-chip. In this review, we try to adopt a more holistic approach and cover different uses of microfluidics for a variety of applications, specifically aimed at sperm separation and analysis. We present various sperm separation microfluidic techniques, categorized as natural and non-natural methods. A few of the recent developments in on-chip fertilization are also discussed.
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Affiliation(s)
| | - Zahra Saeidpour
- MEMS Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran
| | - Zahra Habibi
- MEMS Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran
| | - Saeed Javadizadeh
- MEMS Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran
| | - Seyed Ahmadreza Firoozabadi
- MEMS Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran
| | - Majid Badieirostami
- MEMS Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, 1439957131, Iran.
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6
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Hanassab S, Abbara A, Yeung AC, Voliotis M, Tsaneva-Atanasova K, Kelsey TW, Trew GH, Nelson SM, Heinis T, Dhillo WS. The prospect of artificial intelligence to personalize assisted reproductive technology. NPJ Digit Med 2024; 7:55. [PMID: 38429464 PMCID: PMC10907618 DOI: 10.1038/s41746-024-01006-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 01/10/2024] [Indexed: 03/03/2024] Open
Abstract
Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.
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Affiliation(s)
- Simon Hanassab
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- Department of Computing, Imperial College London, London, UK
- UKRI Centre for Doctoral Training in AI for Healthcare, Imperial College London, London, UK
| | - Ali Abbara
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Arthur C Yeung
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- Imperial College Healthcare NHS Trust, London, UK
| | - Margaritis Voliotis
- Department of Mathematics and Statistics, University of Exeter, Exeter, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK
| | - Krasimira Tsaneva-Atanasova
- Department of Mathematics and Statistics, University of Exeter, Exeter, UK
- Living Systems Institute, University of Exeter, Exeter, UK
- EPSRC Hub for Quantitative Modelling in Healthcare, University of Exeter, Exeter, UK
| | - Tom W Kelsey
- School of Computer Science, University of St Andrews, St Andrews, UK
| | - Geoffrey H Trew
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK
- The Fertility Partnership, Oxford, UK
| | - Scott M Nelson
- The Fertility Partnership, Oxford, UK
- School of Medicine, University of Glasgow, Glasgow, UK
- Biomedical Research Centre, University of Bristol, Bristol, UK
| | - Thomas Heinis
- Department of Computing, Imperial College London, London, UK
| | - Waljit S Dhillo
- Department of Metabolism, Digestion, and Reproduction, Imperial College London, London, UK.
- Imperial College Healthcare NHS Trust, London, UK.
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7
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Panner Selvam MK, Moharana AK, Baskaran S, Finelli R, Hudnall MC, Sikka SC. Current Updates on Involvement of Artificial Intelligence and Machine Learning in Semen Analysis. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:279. [PMID: 38399566 PMCID: PMC10890589 DOI: 10.3390/medicina60020279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Revised: 01/29/2024] [Accepted: 01/31/2024] [Indexed: 02/25/2024]
Abstract
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.
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Affiliation(s)
- Manesh Kumar Panner Selvam
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
| | - Ajaya Kumar Moharana
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
- Redox Biology & Proteomics Laboratory, Department of Zoology, School of Life Sciences, Ravenshaw University, Cuttack 753003, Odisha, India
| | - Saradha Baskaran
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
| | | | | | - Suresh C. Sikka
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA; (A.K.M.); (S.B.); (S.C.S.)
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8
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Yang Z, Zhang L, Fan H, Yan B, Mu Y, Zhou Y, Pei C, Li L, Xiao X. Gaussian clustering and quantification of the sperm chromatin dispersion test using convolutional neural networks. Analyst 2024; 149:366-375. [PMID: 38044817 DOI: 10.1039/d3an01616a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Sperm DNA fragmentation is a sign of sperm nuclear damage. The sperm chromatin dispersion (SCD) test is a reliable and economical method for the evaluation of DNA fragmentation. However, the cut-off value for differentiation of DNA fragmented sperms is fixed at 1/3 with limited statistical justification, making the SCD test a semi-quantitative method that gives user-dependent results. We construct a collection of deep neural networks to automate the evaluation of bright-field images for SCD tests. The model can detect valid sperm nuclei and their locations from the input images captured with a 20× objective and predict the geometric parameters of the halo ring. We construct an annotated dataset consisting of N = 3120 images. The ResNet 18 based network reaches an average precision (AP50) of 91.3%, a true positive rate of 96.67%, and a true negative rate of 96.72%. The distribution of relative halo radii is fit to the multi-peak Gaussian function (p > 0.99). DNA fragmentation is regarded as those with a relative halo radius 1.6 standard deviations smaller than the mean of a normal cluster. In conclusion, we have established a deep neural network based model for the automation and quantification of the SCD test that is ready for clinical application. The DNA fragmentation index is determined using Gaussian clustering, reflecting the natural distribution of halo geometry and is more tolerable to disturbances and sample conditions, which we believe will greatly improve the clinical significance of the SCD test.
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Affiliation(s)
- Zheng Yang
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China.
| | - Lei Zhang
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China.
| | - Heng Fan
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China.
| | - Bei Yan
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China.
- Ningxia Human Sperm Bank, Institute of Medical Sciences, General Hospital of Ningxia Medical University, Yinchuan, 750004, PR China
| | - Yaoqin Mu
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China.
| | - Yue Zhou
- Ningxia Human Sperm Bank, Institute of Medical Sciences, General Hospital of Ningxia Medical University, Yinchuan, 750004, PR China
| | - Chengbin Pei
- Ningxia Human Sperm Bank, Institute of Medical Sciences, General Hospital of Ningxia Medical University, Yinchuan, 750004, PR China
| | - Longjie Li
- School of Life Science and Technology, Wuhan Polytechnic University, Wuhan, 430023, PR China.
| | - Xianjin Xiao
- Institute of Reproductive Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, PR China.
- Hunan Provincial Key Laboratory of Regional Hereditary Birth Defects Prevention and Control, Changsha Hospital for Maternal and Child Health Care Affiliated to Hunan Normal University, Changsha, PR China
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9
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Ghayda RA, Cannarella R, Calogero AE, Shah R, Rambhatla A, Zohdy W, Kavoussi P, Avidor-Reiss T, Boitrelle F, Mostafa T, Saleh R, Toprak T, Birowo P, Salvio G, Calik G, Kuroda S, Kaiyal RS, Ziouziou I, Crafa A, Phuoc NHV, Russo GI, Durairajanayagam D, Al-Hashimi M, Hamoda TAAAM, Pinggera GM, Adriansjah R, Maldonado Rosas I, Arafa M, Chung E, Atmoko W, Rocco L, Lin H, Huyghe E, Kothari P, Solorzano Vazquez JF, Dimitriadis F, Garrido N, Homa S, Falcone M, Sabbaghian M, Kandil H, Ko E, Martinez M, Nguyen Q, Harraz AM, Serefoglu EC, Karthikeyan VS, Tien DMB, Jindal S, Micic S, Bellavia M, Alali H, Gherabi N, Lewis S, Park HJ, Simopoulou M, Sallam H, Ramirez L, Colpi G, Agarwal A. Artificial Intelligence in Andrology: From Semen Analysis to Image Diagnostics. World J Mens Health 2024; 42:39-61. [PMID: 37382282 PMCID: PMC10782130 DOI: 10.5534/wjmh.230050] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/10/2023] [Accepted: 03/17/2023] [Indexed: 06/30/2023] Open
Abstract
Artificial intelligence (AI) in medicine has gained a lot of momentum in the last decades and has been applied to various fields of medicine. Advances in computer science, medical informatics, robotics, and the need for personalized medicine have facilitated the role of AI in modern healthcare. Similarly, as in other fields, AI applications, such as machine learning, artificial neural networks, and deep learning, have shown great potential in andrology and reproductive medicine. AI-based tools are poised to become valuable assets with abilities to support and aid in diagnosing and treating male infertility, and in improving the accuracy of patient care. These automated, AI-based predictions may offer consistency and efficiency in terms of time and cost in infertility research and clinical management. In andrology and reproductive medicine, AI has been used for objective sperm, oocyte, and embryo selection, prediction of surgical outcomes, cost-effective assessment, development of robotic surgery, and clinical decision-making systems. In the future, better integration and implementation of AI into medicine will undoubtedly lead to pioneering evidence-based breakthroughs and the reshaping of andrology and reproductive medicine.
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Affiliation(s)
- Ramy Abou Ghayda
- Urology Institute, University Hospitals, Case Western Reserve University, Cleveland, OH, USA
| | - Rossella Cannarella
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
- Glickman Urological & Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Aldo E. Calogero
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Rupin Shah
- Department of Urology, Lilavati Hospital and Research Centre, Mumbai, India
| | - Amarnath Rambhatla
- Department of Urology, Henry Ford Health System, Vattikuti Urology Institute, Detroit, MI, USA
| | - Wael Zohdy
- Andrology and STDs, Cairo University, Cairo, Egypt
| | - Parviz Kavoussi
- Department of Urology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
| | - Tomer Avidor-Reiss
- Department of Biological Sciences, University of Toledo, Toledo, OH, USA
- Department of Urology, College of Medicine and Life Sciences, University of Toledo, Toledo, OH, USA
| | - Florence Boitrelle
- Reproductive Biology, Fertility Preservation, Andrology, CECOS, Poissy Hospital, Poissy, France
- Department of Biology, Reproduction, Epigenetics, Environment, and Development, Paris Saclay University, UVSQ, INRAE, BREED, Paris, France
| | - Taymour Mostafa
- Andrology, Sexology & STIs Department, Faculty of Medicine, Cairo University, Cairo, Egypt
| | - Ramadan Saleh
- Department of Dermatology, Venereology and Andrology, Faculty of Medicine, Sohag University, Sohag, Egypt
| | - Tuncay Toprak
- Department of Urology, Fatih Sultan Mehmet Training and Research Hospital, University of Health Sciences, Istanbul, Turkey
| | - Ponco Birowo
- Department of Urology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Gianmaria Salvio
- Department of Endocrinology, Polytechnic University of Marche, Ancona, Italy
| | - Gokhan Calik
- Department of Urology, Istanbul Medipol University, Istanbul, Turkey
| | - Shinnosuke Kuroda
- Glickman Urological & Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
- Department of Urology, Reproduction Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Raneen Sawaid Kaiyal
- Glickman Urological & Kidney Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Imad Ziouziou
- Department of Urology, College of Medicine and Pharmacy, Ibn Zohr University, Agadir, Morocco
| | - Andrea Crafa
- Department of Clinical and Experimental Medicine, University of Catania, Catania, Italy
| | - Nguyen Ho Vinh Phuoc
- Department of Andrology, Binh Dan Hospital, Ho Chi Minh City, Vietnam
- Department of Urology and Andrology, Pham Ngoc Thach University of Medicine, Ho Chi Minh City, Vietnam
| | | | - Damayanthi Durairajanayagam
- Department of Physiology, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Selangor, Malaysia
| | - Manaf Al-Hashimi
- Department of Urology, Burjeel Hospital, Abu Dhabi, United Arab Emirates (UAE)
- Khalifa University, College of Medicine and Health Science, Abu Dhabi, United Arab Emirates (UAE)
| | - Taha Abo-Almagd Abdel-Meguid Hamoda
- Department of Urology, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Urology, Faculty of Medicine, Minia University, El-Minia, Egypt
| | | | - Ricky Adriansjah
- Department of Urology, Hasan Sadikin General Hospital, Universitas Padjadjaran, Banding, Indonesia
| | | | - Mohamed Arafa
- Department of Urology, Hamad Medical Corporation, Doha, Qatar
- Department of Urology, Weill Cornell Medical-Qatar, Doha, Qatar
| | - Eric Chung
- Department of Urology, Princess Alexandra Hospital, University of Queensland, Brisbane QLD, Australia
| | - Widi Atmoko
- Department of Urology, Dr. Cipto Mangunkusumo Hospital, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
| | - Lucia Rocco
- Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, University of Campania “Luigi Vanvitelli”, Caserta, Italy
| | - Haocheng Lin
- Department of Urology, Peking University Third Hospital, Peking University, Beijing, China
| | - Eric Huyghe
- Department of Urology and Andrology, University Hospital of Toulouse, Toulouse, France
| | - Priyank Kothari
- Department of Urology, B.Y.L. Nair Charitable Hospital, Topiwala National Medical College, Mumbai, India
| | | | - Fotios Dimitriadis
- Department of Urology, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nicolas Garrido
- IVIRMA Global Research Alliance, IVI Foundation, Instituto de Investigación Sanitaria La Fe (IIS La Fe), Valencia, Spain
| | - Sheryl Homa
- Department of Biosciences, University of Kent, Canterbury, United Kingdom
| | - Marco Falcone
- Department of Urology, Molinette Hospital, A.O.U. Città della Salute e della Scienza, University of Turin, Torino, Italy
| | - Marjan Sabbaghian
- Department of Andrology, Reproductive Biomedicine Research Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, Iran
| | | | - Edmund Ko
- Department of Urology, Loma Linda University Health, Loma Linda, CA, USA
| | - Marlon Martinez
- Section of Urology, Department of Surgery, University of Santo Tomas Hospital, Manila, Philippines
| | - Quang Nguyen
- Section of Urology, Department of Surgery, University of Santo Tomas Hospital, Manila, Philippines
- Center for Andrology and Sexual Medicine, Viet Duc University Hospital, Hanoi, Vietnam
- Department of Urology, Andrology and Sexual Medicine, University of Medicine and Pharmacy, Vietnam National University, Hanoi, Vietnam
| | - Ahmed M. Harraz
- Urology and Nephrology Center, Mansoura University, Mansoura, Egypt
- Department of Surgery, Urology Unit, Farwaniya Hospital, Farwaniya, Kuwait
- Department of Urology, Sabah Al Ahmad Urology Center, Kuwait City, Kuwait
| | - Ege Can Serefoglu
- Department of Urology, Biruni University School of Medicine, Istanbul, Turkey
| | | | - Dung Mai Ba Tien
- Department of Andrology, Binh Dan Hospital, Ho Chi Minh City, Vietnam
| | - Sunil Jindal
- Department of Andrology and Reproductive Medicine, Jindal Hospital, Meerut, India
| | - Sava Micic
- Department of Andrology, Uromedica Polyclinic, Belgrade, Serbia
| | - Marina Bellavia
- Andrology and IVF Center, Next Fertility Procrea, Lugano, Switzerland
| | - Hamed Alali
- King Fahad Specialist Hospital, Dammam, Saudi Arabia
| | - Nazim Gherabi
- Andrology Committee of the Algerian Association of Urology, Algiers, Algeria
| | - Sheena Lewis
- Examen Lab Ltd., Northern Ireland, United Kingdom
| | - Hyun Jun Park
- Department of Urology, Pusan National University School of Medicine, Busan, Korea
- Medical Research Institute of Pusan National University Hospital, Busan, Korea
| | - Mara Simopoulou
- Department of Experimental Physiology, School of Health Sciences, Faculty of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Hassan Sallam
- Alexandria University Faculty of Medicine, Alexandria, Egypt
| | - Liliana Ramirez
- IVF Laboratory, CITMER Reproductive Medicine, Mexico City, Mexico
| | - Giovanni Colpi
- Andrology and IVF Center, Next Fertility Procrea, Lugano, Switzerland
| | - Ashok Agarwal
- Global Andrology Forum, Moreland Hills, OH, USA
- Cleveland Clinic, Cleveland, OH, USA
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10
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Yilmaz A, Aydin T, Varol R. Virtual staining for pixel-wise and quantitative analysis of single cell images. Sci Rep 2023; 13:19178. [PMID: 37932315 PMCID: PMC10628122 DOI: 10.1038/s41598-023-45150-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
Immunocytochemical staining of microorganisms and cells has long been a popular method for examining their specific subcellular structures in greater detail. Recently, generative networks have emerged as an alternative to traditional immunostaining techniques. These networks infer fluorescence signatures from various imaging modalities and then virtually apply staining to the images in a digital environment. In numerous studies, virtual staining models have been trained on histopathology slides or intricate subcellular structures to enhance their accuracy and applicability. Despite the advancements in virtual staining technology, utilizing this method for quantitative analysis of microscopic images still poses a significant challenge. To address this issue, we propose a straightforward and automated approach for pixel-wise image-to-image translation. Our primary objective in this research is to leverage advanced virtual staining techniques to accurately measure the DNA fragmentation index in unstained sperm images. This not only offers a non-invasive approach to gauging sperm quality, but also paves the way for streamlined and efficient analyses without the constraints and potential biases introduced by traditional staining processes. This novel approach takes into account the limitations of conventional techniques and incorporates improvements to bolster the reliability of the virtual staining process. To further refine the results, we discuss various denoising techniques that can be employed to reduce the impact of background noise on the digital images. Additionally, we present a pixel-wise image matching algorithm designed to minimize the error caused by background noise and to prevent the introduction of bias into the analysis. By combining these approaches, we aim to develop a more effective and reliable method for quantitative analysis of virtually stained microscopic images, ultimately enhancing the study of microorganisms and cells at the subcellular level.
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Affiliation(s)
- Abdurrahim Yilmaz
- Universität der Bundeswehr München, 85579, Neubiberg, Germany
- Imperial College London, London, SW7 2BX, United Kingdom
| | - Tuelay Aydin
- Universität der Bundeswehr München, 85579, Neubiberg, Germany
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11
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Lewandowska E, Węsierski D, Mazur-Milecka M, Liss J, Jezierska A. Ensembling noisy segmentation masks of blurred sperm images. Comput Biol Med 2023; 166:107520. [PMID: 37804777 DOI: 10.1016/j.compbiomed.2023.107520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 08/11/2023] [Accepted: 09/19/2023] [Indexed: 10/09/2023]
Abstract
BACKGROUND Sperm tail morphology and motility have been demonstrated to be important factors in determining sperm quality for in vitro fertilization. However, many existing computer-aided sperm analysis systems leave the sperm tail out of the analysis, as detecting a few tail pixels is challenging. Moreover, some publicly available datasets for classifying morphological defects contain images limited only to the sperm head. This study focuses on the segmentation of full sperm, which consists of the head and tail parts, and appear alone and in groups. METHODS We re-purpose the Feature Pyramid Network to ensemble an input image with multiple masks from state-of-the-art segmentation algorithms using a scale-specific cross-attention module. We normalize homogeneous backgrounds for improved training. The low field depth of microscopes blurs the images, easily confusing human raters in discerning minuscule sperm from large backgrounds. We thus propose evaluation protocols for scoring segmentation models trained on imbalanced data and noisy ground truth. RESULTS The neural ensembling of noisy segmentation masks outperforms all single, state-of-the-art segmentation algorithms in full sperm segmentation. Human raters agree more on the head than tail masks. The algorithms also segment the head better than the tail. CONCLUSIONS The extensive evaluation of state-of-the-art segmentation algorithms shows that full sperm segmentation is challenging. We release the SegSperm dataset of images from Intracytoplasmic Sperm Injection procedures to spur further progress on full sperm segmentation with noisy and imbalanced ground truth. The dataset is publicly available at https://doi.org/10.34808/6wm7-1159.
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Affiliation(s)
| | - Daniel Węsierski
- Cameras and Algorithms Lab, Gdańsk University of Technology, Poland; Multimedia Systems Department, Faculty of Electronics, Telecommunication, and Informatics, Gdańsk University of Technology, Poland
| | - Magdalena Mazur-Milecka
- Department of Biomedical Engineering, Faculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, Poland
| | - Joanna Liss
- Invicta Research and Development Center, Sopot, Poland; Department of Medical Biology and Genetics, University of Gdańsk, Poland
| | - Anna Jezierska
- Cameras and Algorithms Lab, Gdańsk University of Technology, Poland; Department of Biomedical Engineering, Faculty of Electronics, Telecommunications, and Informatics, Gdańsk University of Technology, Poland; Department of Modelling and Optimization of Dynamical Systems, Systems Research Institute Warsaw, Poland.
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12
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Lustgarten Guahmich N, Borini E, Zaninovic N. Improving outcomes of assisted reproductive technologies using artificial intelligence for sperm selection. Fertil Steril 2023; 120:729-734. [PMID: 37307892 DOI: 10.1016/j.fertnstert.2023.06.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 06/06/2023] [Indexed: 06/14/2023]
Abstract
Within the field of assisted reproductive technology, artificial intelligence has become an attractive tool for potentially improving success rates. Recently, artificial intelligence-based tools for sperm evaluation and selection during intracytoplasmic sperm injection (ICSI) have been explored, mainly to improve fertilization outcomes and decrease variability within ICSI procedures. Although significant advances have been achieved in developing algorithms that track and rank single sperm in real-time during ICSI, the clinical benefits these might have in improving pregnancy rates from a single assisted reproductive technology cycle remain to be established.
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Affiliation(s)
- Nicole Lustgarten Guahmich
- Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York
| | - Elena Borini
- Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York
| | - Nikica Zaninovic
- Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York.
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13
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Si K, Huang B, Jin L. Application of artificial intelligence in gametes and embryos selection. HUM FERTIL 2023; 26:757-777. [PMID: 37705466 DOI: 10.1080/14647273.2023.2256980] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/22/2023] [Indexed: 09/15/2023]
Abstract
Gamete and embryo quality are critical to the success rate of Assisted Reproductive Technology (ART) cycles, but there remains a lack of methods to accurately measure the quality of sperm, oocytes and embryos. The ability of Artificial Intelligence (AI) technology to analyze large amounts of data, especially video and images, is particularly useful in gamete and embryo assessment and selection. The well-trained model has fast calculation speed and high accuracy, which can help embryologists to perform more objective gamete and embryo selection. Various artificial intelligence models have been developed for gamete and embryo assessment, some of which exhibit good performance. In this review, we summarize the latest applications of AI technology in semen analysis, as well as selection for sperm, oocyte and embryo, and discuss the existing problems and development directions of artificial intelligence in this field.
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Affiliation(s)
- Keyi Si
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Bo Huang
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
| | - Lei Jin
- Reproductive Medicine Center, Tongji Hospital, Tongji Medicine College, Huazhong University of Science and Technology, Wuhan, People's Republic of China
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14
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Park M, Yoon H, Kang BH, Lee H, An J, Lee T, Cheong HT, Lee SH. Deep Learning-Based Precision Analysis for Acrosome Reaction by Modification of Plasma Membrane in Boar Sperm. Animals (Basel) 2023; 13:2622. [PMID: 37627413 PMCID: PMC10451478 DOI: 10.3390/ani13162622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/11/2023] [Accepted: 08/12/2023] [Indexed: 08/27/2023] Open
Abstract
The analysis of AR is widely used to detect loss of acrosome in sperm, but the subjective decisions of experts affect the accuracy of the examination. Therefore, we develop an ARCS for objectivity and consistency of analysis using convolutional neural networks (CNNs) trained with various magnification images. Our models were trained on 215 microscopic images at 400× and 438 images at 1000× magnification using the ResNet 50 and Inception-ResNet v2 architectures. These models distinctly recognized micro-changes in the PM of AR sperms. Moreover, the Inception-ResNet v2-based ARCS achieved a mean average precision of over 97%. Our system's calculation of the AR ratio on the test dataset produced results similar to the work of the three experts and could do so more quickly. Our model streamlines sperm detection and AR status determination using a CNN-based approach, replacing laborious tasks and expert assessments. The ARCS offers consistent AR sperm detection, reduced human error, and decreased working time. In conclusion, our study suggests the feasibility and benefits of using a sperm diagnosis artificial intelligence assistance system in routine practice scenarios.
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Affiliation(s)
- Mira Park
- School of Information and Communication Technology, University of Tasmania, Hobart, TAS 7005, Australia
| | - Heemoon Yoon
- School of Information and Communication Technology, University of Tasmania, Hobart, TAS 7005, Australia
| | - Byeong Ho Kang
- School of Information and Communication Technology, University of Tasmania, Hobart, TAS 7005, Australia
| | - Hayoung Lee
- College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Jisoon An
- College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Taehyun Lee
- College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Hee-Tae Cheong
- College of Veterinary Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
| | - Sang-Hee Lee
- College of Animal Life Sciences, Kangwon National University, Chuncheon 24341, Republic of Korea
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15
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Abadjieva D, Georgiev B, Gerzilov V, Tsvetkova I, Taushanova P, Todorova K, Hayrabedyan S. Machine Learning Approach for Muscovy Duck ( Cairina moschata) Semen Quality Assessment. Animals (Basel) 2023; 13:ani13101596. [PMID: 37238026 DOI: 10.3390/ani13101596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 04/30/2023] [Accepted: 05/08/2023] [Indexed: 05/28/2023] Open
Abstract
This study aimed to develop a comprehensive approach for assessing fresh ejaculate from Muscovy duck (Cairina moschata) drakes to fulfil the requirements of artificial insemination in farm practices. The approach combines sperm kinetics (CASA) with non-kinetic parameters, such as vitality, enzyme activities (alkaline phosphatase (AP), creatine kinase (CK), lactate dehydrogenase (LDH), and γ-glutamyl-transferase (GGT)), and total DNA methylation as training features for a set of machine learning (ML) models designed to enhance the predictive capacity of sperm parameters. Samples were classified based on their progressive motility and DNA methylation features, exhibiting significant differences in total and progressive motility, curvilinear velocity (VCL), velocity of the average path (VAP), linear velocity (VSL), amplitude of lateral head displacement (ALH), beat-cross frequency (BCF), and live normal sperm cells in favour of fast motility ones. Additionally, there were significant differences in enzyme activities for AP and CK, with correlations to LDH and GGT levels. Although motility showed no correlation with total DNA methylation, ALH, wobble of the curvilinear trajectory (WOB), and VCL were significantly different in the newly introduced classification for "suggested good quality", where both motility and methylation were high. The performance differences observed while training various ML classifiers using different feature subsets highlight the importance of DNA methylation for achieving more accurate sample quality classification, even though there is no correlation between motility and DNA methylation. The parameters ALH, VCL, triton extracted LDH, and VAP were top-ranking for "suggested good quality" predictions by the neural network and gradient boosting models. In conclusion, integrating non-kinetic parameters into machine-learning-based sample classification offers a promising approach for selecting kinetically and morphologically superior duck sperm samples that might otherwise be hindered by a predominance of lowly methylated cells.
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Affiliation(s)
- Desislava Abadjieva
- Department of Immunoneuroendocrinology, Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Bul. Tzarigradsko Shosse 73, 1113 Sofia, Bulgaria
| | - Boyko Georgiev
- Department of Immunoneuroendocrinology, Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Bul. Tzarigradsko Shosse 73, 1113 Sofia, Bulgaria
| | - Vasko Gerzilov
- Department of Animal Science, Agricultural University, 12, Mendeleev Str., 4000 Plovdiv, Bulgaria
| | - Ilka Tsvetkova
- Reproductive OMICS Laboratory, Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Bul. Tzarigradsko Shosse 73, 1113 Sofia, Bulgaria
| | - Paulina Taushanova
- Department of Immunoneuroendocrinology, Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Bul. Tzarigradsko Shosse 73, 1113 Sofia, Bulgaria
| | - Krassimira Todorova
- Reproductive OMICS Laboratory, Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Bul. Tzarigradsko Shosse 73, 1113 Sofia, Bulgaria
| | - Soren Hayrabedyan
- Reproductive OMICS Laboratory, Institute of Biology and Immunology of Reproduction, Bulgarian Academy of Sciences, Bul. Tzarigradsko Shosse 73, 1113 Sofia, Bulgaria
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16
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Thambawita V, Hicks SA, Storås AM, Nguyen T, Andersen JM, Witczak O, Haugen TB, Hammer HL, Halvorsen P, Riegler MA. VISEM-Tracking, a human spermatozoa tracking dataset. Sci Data 2023; 10:260. [PMID: 37156762 PMCID: PMC10167330 DOI: 10.1038/s41597-023-02173-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Accepted: 04/20/2023] [Indexed: 05/10/2023] Open
Abstract
A manual assessment of sperm motility requires microscopy observation, which is challenging due to the fast-moving spermatozoa in the field of view. To obtain correct results, manual evaluation requires extensive training. Therefore, computer-aided sperm analysis (CASA) has become increasingly used in clinics. Despite this, more data is needed to train supervised machine learning approaches in order to improve accuracy and reliability in the assessment of sperm motility and kinematics. In this regard, we provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet semen preparations with manually annotated bounding-box coordinates and a set of sperm characteristics analyzed by experts in the domain. In addition to the annotated data, we provide unlabeled video clips for easy-to-use access and analysis of the data via methods such as self- or unsupervised learning. As part of this paper, we present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset. As a result, we show that the dataset can be used to train complex DL models to analyze spermatozoa.
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Affiliation(s)
| | - Steven A Hicks
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - Andrea M Storås
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
| | - Thu Nguyen
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | | | | | | | - Hugo L Hammer
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
| | - Pål Halvorsen
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
| | - Michael A Riegler
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
- Oslo Metropolitan University, Oslo, Norway
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17
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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18
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Gill ME, Quaas AM. Looking with new eyes: advanced microscopy and artificial intelligence in reproductive medicine. J Assist Reprod Genet 2023; 40:235-239. [PMID: 36534231 PMCID: PMC9935756 DOI: 10.1007/s10815-022-02693-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022] Open
Abstract
Microscopy has long played a pivotal role in the field of assisted reproductive technology (ART). The advent of artificial intelligence (AI) has opened the door for new approaches to sperm and oocyte assessment and selection, with the potential for improved ART outcomes.
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Affiliation(s)
- Mark E Gill
- Friedrich Miescher Institute for Biomedical Research (FMI), Maulbeerstrasse 66, 4058, Basel, Switzerland.
| | - Alexander M Quaas
- Division of Reproductive Medicine and Gynecological Endocrinology (RME), University Hospital, University of Basel, Basel, Switzerland
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19
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Kumar RS, Sharma S, Halder A, Gupta V. Deep Learning-Based Robust Automated System for Predicting Human Sperm DNA Fragmentation Index. J Hum Reprod Sci 2023; 16:16-21. [PMID: 37305775 PMCID: PMC10256941 DOI: 10.4103/jhrs.jhrs_4_23] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 06/13/2023] Open
Abstract
Background Determining the DNA fragmentation index (DFI) by the sperm chromatin dispersion (SCD) test involves manual counting of stained sperms with halo and no halo. Aims The aim of this study is to build a robust artificial intelligence-based solution to predict the DFI. Settings and Design This is a retrospective experimental study conducted in a secondary in vitro fertilisation setup. Materials and Methods We obtained 24,415 images from 30 patients after the SCD test using a phase-contrast microscope. We classified the dataset into two, binary (halo/no halo) and multiclass (big/medium/small halo/degraded (DEG)/dust). Our approach consists of a training and prediction phase. The 30 patients' images were divided into training (24) and prediction (6) sets. A pre-processing method M was developed to automatically segment the images to detect sperm-like regions and was annotated by three embryologists. Statistical Analysis Used To interpret the findings, the precision-recall curve and F1 score were utilised. Results Binary and multiclass datasets containing 8887 and 15,528 cropped sperm image regions showed an accuracy of 80.15% versus 75.25%. A precision-recall curve was determined and the binary and multiclass datasets obtained an F1 score of 0.81 versus 0.72. A confusion matrix was applied for predicted and actuals for the multiclass approach where small halo and medium halo confusion were found to be highest. Conclusion Our proposed machine learning model can standardise and aid in arriving at accurate results without using expensive software. It provides accurate information about healthy and DEG sperms in a given sample, thereby attaining better clinical outcomes. The binary approach performed better with our model than the multiclass approach. However, the multiclass approach can highlight the distribution of fragmented and non-fragmented sperms.
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Affiliation(s)
| | - Swapnil Sharma
- Linkedin Technology Information Pvt. Ltd., Mumbai, Maharashtra, India
| | | | - Vipin Gupta
- Linkedin Technology Information Pvt. Ltd., Mumbai, Maharashtra, India
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20
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Adler A, Roth B, Lundy SD, Takeshima T, Yumura Y, Kuroda S. Sperm DNA fragmentation testing in clinical management of reproductive medicine. Reprod Med Biol 2023; 22:e12547. [PMID: 37915974 PMCID: PMC10616814 DOI: 10.1002/rmb2.12547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/26/2023] [Accepted: 10/12/2023] [Indexed: 11/03/2023] Open
Abstract
Background Approximately 8%-12% of couples worldwide face infertility, with infertility of individuals assigned male at birth (AMAB) contributing to at least 50% of cases. Conventional semen analysis commonly used to detect sperm abnormalities is insufficient, as 30% of AMAB patients experiencing infertility show normal results in this test. From a genetic perspective, the assessment of sperm DNA fragmentation (SDF) is important as a parameter of sperm quality. Methods In this narrative study, we review and discuss pathophysiological causes, DNA repair mechanisms, and management of high SDF. We then summarize literature exploring the association between SDF and reproductive outcomes. Main Findings Recent systematic reviews and meta-analyses have revealed a significant association between high SDF in AMAB individuals and adverse reproductive outcomes including embryo development, natural conception, intrauterine insemination, and in vitro fertilization. However, the association with live birth rates and pregnancy rates following intracytoplasmic injection remains inconclusive. The disparities among quantitative assays, inconsistent reference range values, absent high-quality prospective clinical trials, and clinical heterogeneity in AMAB patients with elevated SDF represent the main limitations affecting SDF testing. Conclusion The evaluation and management of SDF plays an important role in a subset of AMAB infertility, but widespread integration into clinical guidelines will require future high-quality clinical trials and assay standardization.
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Affiliation(s)
- Ava Adler
- Glickman Urological & Kidney InstituteCleveland Clinic FoundationClevelandOhioUSA
| | - Bradley Roth
- Glickman Urological & Kidney InstituteCleveland Clinic FoundationClevelandOhioUSA
| | - Scott D. Lundy
- Glickman Urological & Kidney InstituteCleveland Clinic FoundationClevelandOhioUSA
| | - Teppei Takeshima
- Department of Urology, Reproduction CenterYokohama City University Medical CenterYokohamaJapan
| | - Yasushi Yumura
- Department of Urology, Reproduction CenterYokohama City University Medical CenterYokohamaJapan
| | - Shinnosuke Kuroda
- Glickman Urological & Kidney InstituteCleveland Clinic FoundationClevelandOhioUSA
- Department of Urology, Reproduction CenterYokohama City University Medical CenterYokohamaJapan
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21
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Schardein JN, Fendereski K, Hotaling JM. Evolution of the basic semen analysis and processing sperm. Curr Opin Urol 2023; 33:16-23. [PMID: 36226727 DOI: 10.1097/mou.0000000000001054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The sixth edition of the World Health Organization (WHO) laboratory manual for the examination and processing of human semen was recently published with specific step-by-step instructions for semen evaluation and sperm processing. Point-of-care (POC) testing for semen evaluation and microfluidics for sperm processing are rapidly evolving technologies that could impact how we evaluate and process sperm. Understanding the updated manual in the context of these novel technologies is important. RECENT FINDINGS Proper standardization of semen evaluation and sperm processing will allow for consistent high-quality results among laboratories worldwide. POC testing could improve access to semen evaluations that generate referrals to male infertility specialists for further assessment. Microfluidics can select functional sperm with decreased DNA fragmentation in semen and testicular biopsy samples for assisted reproductive technology (ART). Clinical outcomes, such as pregnancy rates and live birth rates, have not been shown to be consistently improved with these technologies compared to conventional techniques, although high level evidence research in this area is limited. SUMMARY POC testing and microfluidics have the potential to be combined with machine learning technologies to improve fertility care. If these technologies are appropriately optimized, they could change how we evaluate and process sperm, and potentially lead to improved ART outcomes.
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Affiliation(s)
- Jessica N Schardein
- Department of Surgery, Division of Urology, University of Utah, Salt Lake City, Utah, USA
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22
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Leung ETY, Lee BKM, Lee CL, Tian X, Lam KKW, Li RHW, Ng EHY, Yeung WSB, Ou JP, Chiu PCN. The role of spermatozoa-zona pellucida interaction in selecting fertilization-competent spermatozoa in humans. Front Endocrinol (Lausanne) 2023; 14:1135973. [PMID: 37020592 PMCID: PMC10067631 DOI: 10.3389/fendo.2023.1135973] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 03/07/2023] [Indexed: 03/22/2023] Open
Abstract
Human fertilization begins when a capacitated spermatozoon binds to the zona pellucida (ZP) surrounding a mature oocyte. Defective spermatozoa-ZP interaction contributes to male infertility and is a leading cause of reduced fertilization rates in assisted reproduction treatments (ARTs). Human ejaculate contains millions of spermatozoa with varying degrees of fertilization potential and genetic quality, of which only thousands of motile spermatozoa can bind to the ZP at the fertilization site. This observation suggests that human ZP selectively interacts with competitively superior spermatozoa characterized by high fertilizing capability and genetic integrity. However, direct evidence for ZP-mediated sperm selection process is lacking. This study aims to demonstrate that spermatozoa-ZP interaction represents a crucial step in selecting fertilization-competent spermatozoa in humans. ZP-bound and unbound spermatozoa were respectively collected by a spermatozoa-ZP coincubation assay. The time-course data demonstrated that ZP interacted with a small proportion of motile spermatozoa. Heat shock 70 kDa protein 2 (HSPA2) and sperm acrosome associated 3 (SPACA 3) are two protein markers associated with the sperm ZP-binding ability. Immunofluorescent staining indicated that the ZP-bound spermatozoa had significantly higher expression levels of HSPA2 and SPACA3 than the unbound spermatozoa. ZP-bound spermatozoa had a significantly higher level of normal morphology, DNA integrity, chromatin integrity, protamination and global methylation when compared to the unbound spermatozoa. The results validated the possibility of applying spermatozoa-ZP interaction to select fertilization-competent spermatozoa in ART. This highly selective interaction might also provide diagnostic information regarding the fertilization potential and genetic qualities of spermatozoa independent of those derived from the standard semen analysis.
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Affiliation(s)
- Erica T. Y. Leung
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Brayden K. M. Lee
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Cheuk-Lun Lee
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Shenzhen Key Laboratory of Fertility Regulation, The University of Hong Kong – Shenzhen Hospital, Shenzhen, China
| | - Xinyi Tian
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Kevin K. W. Lam
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Shenzhen Key Laboratory of Fertility Regulation, The University of Hong Kong – Shenzhen Hospital, Shenzhen, China
| | - Raymond H. W. Li
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Shenzhen Key Laboratory of Fertility Regulation, The University of Hong Kong – Shenzhen Hospital, Shenzhen, China
| | - Ernest H. Y. Ng
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Shenzhen Key Laboratory of Fertility Regulation, The University of Hong Kong – Shenzhen Hospital, Shenzhen, China
| | - William S. B. Yeung
- Shenzhen Key Laboratory of Fertility Regulation, The University of Hong Kong – Shenzhen Hospital, Shenzhen, China
| | - Jian-Ping Ou
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Center for Reproductive Medicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
- *Correspondence: Philip C. N. Chiu, ; Jian-Ping Ou,
| | - Philip C. N. Chiu
- Department of Obstetrics and Gynaecology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Shenzhen Key Laboratory of Fertility Regulation, The University of Hong Kong – Shenzhen Hospital, Shenzhen, China
- *Correspondence: Philip C. N. Chiu, ; Jian-Ping Ou,
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23
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Grigorashvili EI, Chervontseva ZS, Gelfand MS. Predicting RNA secondary structure by a neural network: what features may be learned? PeerJ 2022; 10:e14335. [PMID: 36530406 PMCID: PMC9756865 DOI: 10.7717/peerj.14335] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 10/12/2022] [Indexed: 12/14/2022] Open
Abstract
Deep learning is a class of machine learning techniques capable of creating internal representation of data without explicit preprogramming. Hence, in addition to practical applications, it is of interest to analyze what features of biological data may be learned by such models. Here, we describe PredPair, a deep learning neural network trained to predict base pairs in RNA structure from sequence alone, without any incorporated prior knowledge, such as the stacking energies or possible spatial structures. PredPair learned the Watson-Crick and wobble base-pairing rules and created an internal representation of the stacking energies and helices. Application to independent experimental (DMS-Seq) data on nucleotide accessibility in mRNA showed that the nucleotides predicted as paired indeed tend to be involved in the RNA structure. The performance of the constructed model was comparable with the state-of-the-art method based on the thermodynamic approach, but with a higher false positives rate. On the other hand, it successfully predicted pseudoknots. t-SNE clusters of embeddings of RNA sequences created by PredPair tend to contain embeddings from particular Rfam families, supporting the predictions of PredPair being in line with biological classification.
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Affiliation(s)
| | | | - Mikhail S. Gelfand
- Center of Molecular and Cellular Biology, Skolkovo Institute of Science and Technology, Moscow, Russia,Institute of Information Transmission Problems, Moscow, Russia
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24
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Diaz P, Dullea A, Chu KY, Zizzo J, Loloi J, Reddy R, Campbell K, Li PS, Ramasamy R. Future of Male Infertility Evaluation and Treatment: Brief Review of Emerging Technology. Urology 2022; 169:9-16. [PMID: 35905774 DOI: 10.1016/j.urology.2022.06.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 05/29/2022] [Accepted: 06/12/2022] [Indexed: 11/19/2022]
Abstract
Over the past few decades, there have been significant advances in male infertility, particularly in the development of novel diagnostic tools. Unfortunately, there remains a substantial number of patients that remain infertile despite these improvements. In this review, we take heed of the emerging technologies that will shape the future of male infertility diagnosis, evaluation and treatment. Improvement in computer-assisted semen analyses and portability allow males to obtain basic semen parameters from the comfort of their home. Additionally, breakthrough ultrasound technology allows for preoperative prediction of potential areas of spermatogenesis within the testes, high-resolution optics permits better visualization during microdissection testicular sperm extraction (mTESE), and artificial intelligence improves sperm selection and identification.
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Affiliation(s)
- Parris Diaz
- University of Miami, Miller School of Medicine, Department of Urology, Miami, FL
| | - Alexandra Dullea
- University of Miami, Miller School of Medicine, Department of Urology, Miami, FL
| | - Kevin Y Chu
- University of Miami, Miller School of Medicine, Department of Urology, Miami, FL.
| | - John Zizzo
- University of Miami, Miller School of Medicine, Department of Urology, Miami, FL
| | - Justin Loloi
- Montefiore Medical Center, Department of Urology, Bronx, NY
| | - Rohit Reddy
- University of Miami, Miller School of Medicine, Department of Urology, Miami, FL
| | | | - Philip S Li
- Weill Cornell Medicine, Department of Urology, New York, NY
| | - Ranjith Ramasamy
- University of Miami, Miller School of Medicine, Department of Urology, Miami, FL
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25
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Lee R, Witherspoon L, Robinson M, Lee JH, Duffy SP, Flannigan R, Ma H. Automated rare sperm identification from low-magnification microscopy images of dissociated microsurgical testicular sperm extraction samples using deep learning. Fertil Steril 2022; 118:90-99. [PMID: 35562203 DOI: 10.1016/j.fertnstert.2022.03.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 03/15/2022] [Accepted: 03/18/2022] [Indexed: 01/16/2023]
Abstract
OBJECTIVE To develop a machine learning algorithm to detect rare human sperm in semen and microsurgical testicular sperm extraction (microTESE) samples using bright-field (BF) microscopy for nonobstructive azoospermia patients. DESIGN Spermatozoa were collected from fertile men. Testis biopsies were collected from microTESE samples determined to be clinically negative for sperm. A convolutional neural network based on the U-Net architecture was trained using 35,761 BF image patches with fluorescent ground truth image pairs to segment sperm. The algorithm was validated using 7,663 image patches. The algorithm was tested using 7,663 image patches containing abundant sperm, as well as 7,985 image patches containing rare sperm. SETTING In vitro fertilization center and university laboratories. PATIENT(S) Normospermic and nonobstructive azoospermia patients. INTERVENTION(S) None. MAIN OUTCOME MEASURE(S) Precision (positive predictive value [PPV]), recall (sensitivity), and F1-score of detected sperm locations. RESULT(S) For sperm-only samples, our algorithm achieved 91% PPV, 95.8% sensitivity, and 93.3% F1-score at ×10 magnification. For dissociated microTESE samples doped with an abundant quantity of sperm, our algorithm achieved 84.0% PPV, 72.7% sensitivity, and 77.9% F1-score. For dissociated microTESE samples doped with rare sperm, our algorithm achieved 84.4% PPV, 86.1% sensitivity, and 85.2% F1-score. CONCLUSION(S) Rare sperm can be detected in patients' testis biopsy samples for potential subsequent use in in vitro fertilization-intracytoplasmic sperm injection. A machine learning algorithm can use BF images at ×10 magnification to accurately detect sperm locations using automated imaging.
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Affiliation(s)
- Ryan Lee
- Department of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Blood Research, University of British Columbia, Vancouver, British Columbia, Canada; Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada
| | - Luke Witherspoon
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada; Department of Urology, The Ottawa Hospital, Ottawa, Ontario, Canada
| | - Meghan Robinson
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada
| | - Jeong Hyun Lee
- Department of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Blood Research, University of British Columbia, Vancouver, British Columbia, Canada
| | - Simon P Duffy
- Department of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Blood Research, University of British Columbia, Vancouver, British Columbia, Canada
| | - Ryan Flannigan
- Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada; Department of Urology, Weill Cornell Medicine, New York, New York.
| | - Hongshen Ma
- Department of Mechanical Engineering, University of British Columbia, Vancouver, British Columbia, Canada; Centre for Blood Research, University of British Columbia, Vancouver, British Columbia, Canada; Department of Urologic Sciences, University of British Columbia, Vancouver, British Columbia, Canada; Vancouver Prostate Centre, Vancouver General Hospital, Vancouver, British Columbia, Canada; School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia, Canada.
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26
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Botezatu A, Vladoiu S, Fudulu A, Albulescu A, Plesa A, Muresan A, Stancu C, Iancu IV, Diaconu CC, Velicu A, Popa OM, Badiu C, Dinu-Draganescu D. Advanced molecular approaches in male infertility diagnosis. Biol Reprod 2022; 107:684-704. [PMID: 35594455 DOI: 10.1093/biolre/ioac105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 04/29/2022] [Accepted: 05/11/2022] [Indexed: 11/13/2022] Open
Abstract
In the recent years a special attention has been given to a major health concern namely to male infertility, defined as the inability to conceive after 12 months of regular unprotected sexual intercourse, taken into account the statistics that highlight that sperm counts have dropped by 50-60% in recent decades. According to the WHO, infertility affects approximately 9% of couples globally, and the male factor is believed to be present in roughly 50% of cases, with exclusive responsibility in 30%. The aim of this manuscript is to present an evidence-based approach for diagnosing male infertility that includes finding new solutions for diagnosis and critical outcomes, retrieving up-to-date studies and existing guidelines. The diverse factors that induce male infertility generated in a vast amount of data that needed to be analysed by a clinician before a decision could be made for each individual. Modern medicine faces numerous obstacles as a result of the massive amount of data generated by the molecular biology discipline. To address complex clinical problems, vast data must be collected, analysed, and used, which can be very challenging. The use of artificial intelligence (AI) methods to create a decision support system can help predict the diagnosis and guide treatment for infertile men, based on analysis of different data as environmental and lifestyle, clinical (sperm count, morphology, hormone testing, karyotype, etc.) and "omics" bigdata. Ultimately, the development of AI algorithms will assist clinicians in formulating diagnosis, making treatment decisions, and predicting outcomes for assisted reproduction techniques.
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Affiliation(s)
- A Botezatu
- "Stefan S. Nicolau" Institute of Virology, Bucharest, Romania
| | - S Vladoiu
- "CI Parhon" National Institute of Endocrinology, Bucharest, Romania
| | - A Fudulu
- "Stefan S. Nicolau" Institute of Virology, Bucharest, Romania
| | - A Albulescu
- "Stefan S. Nicolau" Institute of Virology, Bucharest, Romania.,National Institute for Chemical pharmaceutical Research & Development
| | - A Plesa
- "Stefan S. Nicolau" Institute of Virology, Bucharest, Romania
| | - A Muresan
- "CI Parhon" National Institute of Endocrinology, Bucharest, Romania
| | - C Stancu
- "CI Parhon" National Institute of Endocrinology, Bucharest, Romania
| | - I V Iancu
- "Stefan S. Nicolau" Institute of Virology, Bucharest, Romania
| | - C C Diaconu
- "Stefan S. Nicolau" Institute of Virology, Bucharest, Romania
| | - A Velicu
- "CI Parhon" National Institute of Endocrinology, Bucharest, Romania
| | - O M Popa
- "CI Parhon" National Institute of Endocrinology, Bucharest, Romania
| | - C Badiu
- "CI Parhon" National Institute of Endocrinology, Bucharest, Romania.,"Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania
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27
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Lu JC. High DNA stainability (HDS) should not be recommended as a marker for the detection of sperm DNA damage. Andrologia 2022; 54:e14442. [PMID: 35415912 DOI: 10.1111/and.14442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/06/2022] [Accepted: 03/30/2022] [Indexed: 11/01/2022] Open
Abstract
There was a marker of high DNA stainability (HDS) in the detection of sperm DNA damage, which was defined as the sperm with high green stainability (HIGRN). The sperm with normal double-stranded DNA was stained green by acridine orange (AO). However, the sperm with high green fluorescence or HDS were thought of as immature sperm or the sperm with poor chromatin condensation. Some previous literature reported that the proportion of sperm with HDS increased with age, and had a certain correlation with the poor outcome of assisted reproductive technology. Recently, several articles reported that the marker of HDS decreased linearly with age, which was obviously inconsistent with that reported by the previous literature. In this case, what kind of marker is HDS? Is it worth studying? After extensively reading the literature related to HDS and flow cytometry related books and performing a series of studies related to the detection of sperm DNA damage, we believe that the establishment of HDS in the detection of sperm DNA damage has no theoretical basis and also no support of evidence-based medicine and that using HDS as a marker in the detection of sperm DNA integrity is inappropriate.
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Affiliation(s)
- Jin-Chun Lu
- Center for Reproductive Medicine, Zhongda Hospital, Southeast University, Nanjing, China
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28
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Computer software (SiD) assisted real-time single sperm selection correlates with fertilization and blastocyst formation. Reprod Biomed Online 2022; 45:703-711. [DOI: 10.1016/j.rbmo.2022.03.036] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 03/22/2022] [Accepted: 03/30/2022] [Indexed: 11/22/2022]
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29
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Current Applications of Machine Learning in Medicine: ART. Artif Intell Med 2022. [DOI: 10.1007/978-981-19-1223-8_6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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30
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Zheng J, Cole T, Zhang Y, Kim J, Tang SY. Exploiting machine learning for bestowing intelligence to microfluidics. Biosens Bioelectron 2021; 194:113666. [PMID: 34600338 DOI: 10.1016/j.bios.2021.113666] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 02/06/2023]
Abstract
Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.
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Affiliation(s)
- Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Jeeson Kim
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, South Korea.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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31
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Abstract
Sperm selection in the female reproductive tract (FRT) is sophisticated. Only about 1,000 sperm out of millions in an ejaculate reach the fallopian tube and thus have a chance of fertilizing an oocyte. In assisted reproduction techniques, sperm are usually selected using their density or motility, characteristics that do not reflect their fertilization competence and, therefore, might result in failure to fertilize the oocyte. Although sperm processing in in vitro fertilization (IVF) and intrauterine insemination (IUI) bypasses many of the selection processes in the FRT, selection by the cumulus mass and the zona pellucida remain intact. By contrast, the direct injection of a sperm into an oocyte in intracytoplasmic sperm injection (ICSI) bypasses all natural selection barriers and, therefore, increases the risk of transferring paternal defects such as fragmented DNA and genomic abnormalities in sperm to the resulting child. Research into surrogate markers of fertilization potential and into simulating the natural sperm selection processes has progressed. However, methods of sperm isolation - such as hyaluronic acid-based selection and microfluidic isolation based on sperm tactic responses - use only one or two parameters and are not comparable with the multistep sperm selection processes naturally occurring within the FRT. Fertilization-competent sperm require a panel of molecules, including zona pellucida-binding proteins and ion channel proteins, that enable them to progress through the FRT to achieve fertilization. The optimal artificial sperm selection method will, therefore, probably need to use a multiparameter tool that incorporates the molecular signature of sperm with high fertilization potential, and their responses to external cues, within a microfluidic system that can replicate the physiological processes of the FRT in vitro.
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32
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Dai C, Zhang Z, Shan G, Chu LT, Huang Z, Moskovtsev S, Librach C, Jarvi K, Sun Y. Advances in sperm analysis: techniques, discoveries and applications. Nat Rev Urol 2021; 18:447-467. [PMID: 34075227 DOI: 10.1038/s41585-021-00472-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/20/2021] [Indexed: 02/05/2023]
Abstract
Infertility affects one in six couples worldwide, and fertility continues to deteriorate globally, partly owing to a decline in semen quality. Sperm analysis has a central role in diagnosing and treating male factor infertility. Many emerging techniques, such as digital holography, super-resolution microscopy and next-generation sequencing, have been developed that enable improved analysis of sperm motility, morphology and genetics to help overcome limitations in accuracy and consistency, and improve sperm selection for infertility treatment. These techniques have also improved our understanding of fundamental sperm physiology by enabling discoveries in sperm behaviour and molecular structures. Further progress in sperm analysis and integrating these techniques into laboratories and clinics requires multidisciplinary collaboration, which will increase discovery and improve clinical outcomes.
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Affiliation(s)
- Changsheng Dai
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Zhuoran Zhang
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Guanqiao Shan
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Lap-Tak Chu
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | - Zongjie Huang
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada
| | | | | | - Keith Jarvi
- Division of Urology, Mount Sinai Hospital, Toronto, Canada. .,Department of Surgery, University of Toronto, Toronto, Canada.
| | - Yu Sun
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Canada. .,Institute of Biomaterials & Biomedical Engineering, University of Toronto, Toronto, Canada. .,Department of Electrical and Computer Engineering, University of Toronto, Toronto, Canada. .,Department of Computer Science, University of Toronto, Toronto, Canada.
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33
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Riegler MA, Stensen MH, Witczak O, Andersen JM, Hicks SA, Hammer HL, Delbarre E, Halvorsen P, Yazidi A, Holst N, Haugen TB. Artificial intelligence in the fertility clinic: status, pitfalls and possibilities. Hum Reprod 2021; 36:2429-2442. [PMID: 34324672 DOI: 10.1093/humrep/deab168] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 06/21/2021] [Indexed: 12/15/2022] Open
Abstract
In recent years, the amount of data produced in the field of ART has increased exponentially. The diversity of data is large, ranging from videos to tabular data. At the same time, artificial intelligence (AI) is progressively used in medical practice and may become a promising tool to improve success rates with ART. AI models may compensate for the lack of objectivity in several critical procedures in fertility clinics, especially embryo and sperm assessments. Various models have been developed, and even though several of them show promising performance, there are still many challenges to overcome. In this review, we present recent research on AI in the context of ART. We discuss the strengths and weaknesses of the presented methods, especially regarding clinical relevance. We also address the pitfalls hampering successful use of AI in the clinic and discuss future possibilities and important aspects to make AI truly useful for ART.
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Affiliation(s)
- M A Riegler
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | | | - O Witczak
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - J M Andersen
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - S A Hicks
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - H L Hammer
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - E Delbarre
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - P Halvorsen
- Department of Holistic Systems, Simula Metropolitan Center for Digital Engineering, Oslo, Norway.,Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - A Yazidi
- Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway
| | - N Holst
- Fertilitetssenteret, Oslo, Norway
| | - T B Haugen
- Department of Life Sciences and Health, Faculty of Health Sciences, OsloMet-Oslo Metropolitan University, Oslo, Norway
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34
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Ueno S, Berntsen J, Ito M, Uchiyama K, Okimura T, Yabuuchi A, Kato K. Pregnancy prediction performance of an annotation-free embryo scoring system on the basis of deep learning after single vitrified-warmed blastocyst transfer: a single-center large cohort retrospective study. Fertil Steril 2021; 116:1172-1180. [PMID: 34246469 DOI: 10.1016/j.fertnstert.2021.06.001] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 12/26/2022]
Abstract
OBJECTIVE To analyze the performance of an annotation-free embryo scoring system on the basis of deep learning for pregnancy prediction after single vitrified blastocyst transfer (SVBT) compared with the performance of other blastocyst grading systems dependent on annotation or morphology scores. DESIGN A single-center large cohort retrospective study from an independent validation test. SETTING Infertility clinic. PATIENT(S) Patients who underwent SVBT cycles (3,018 cycles, mean ± SD patient age 39.3 ± 4.0 years). INTERVENTION(S) None. MAIN OUTCOME MEASURE(S) The pregnancy prediction performances of each embryo scoring model were compared using the area under curve (AUC) for predicting the fetal heartbeat status for each maternal age group. RESULT(S) The AUCs of the <35 years age group (n = 389) for pregnancy prediction were 0.72 for iDAScore, 0.66 for KIDScore, and 0.64 for the Gardner criteria. The AUC of iDAScore was significantly greater than those of the other two models. For the 35-37 years age group (n = 514), the AUCs were 0.68, 0.68, and 0.65 for iDAScore, KIDScore, and the Gardner criteria, respectively, and were not significantly different. The AUCs of the 38-40 years age group (n = 796) were 0.67 for iDAScore, 0.65 for KIDScore, and 0.64 for the Gardner criteria, and there were no significant differences. The AUCs of the 41-42 years age group (n = 636) were 0.66, 0.66, and 0.63 for iDAScore, KIDScore, and the Gardner criteria, respectively, and there were no significant differences among the pregnancy prediction models. For the >42 years age group (n = 389), the AUCs were 0.76 for iDAScore, 0.75 for KIDScore, and 0.75 for the Gardner criteria, and there were no significant differences. Thus, iDAScore AUC was either the highest or equal to the highest AUC for all age groups, although a significant difference was observed only in the youngest age group. CONCLUSION(S) Our results showed that objective embryo assessment by a completely automatic and annotation-free model, iDAScore, performed as well as or even better than more traditional embryo assessment or annotation-dependent ranking tools. iDAScore could be an optimal pregnancy prediction model after SVBT, especially in young patients.
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You JB, McCallum C, Wang Y, Riordon J, Nosrati R, Sinton D. Machine learning for sperm selection. Nat Rev Urol 2021; 18:387-403. [PMID: 34002070 DOI: 10.1038/s41585-021-00465-1] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/30/2021] [Indexed: 02/04/2023]
Abstract
Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has remained at ~33%. Machine learning, an automated method of data analysis based on patterns and inference, is increasingly being deployed within the health-care sector to improve diagnostics and therapeutics. This technique is already aiding embryo selection in some fertility clinics, and has also been applied in research laboratories to improve sperm analysis and selection. Tremendous opportunities exist for machine learning to advance male fertility treatments. The fundamental challenge of sperm selection - selecting the most promising candidate from 108 gametes - presents a challenge that is uniquely well-suited to the high-throughput capabilities of machine learning algorithms paired with modern data processing capabilities.
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Affiliation(s)
- Jae Bem You
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada.,Department of Chemical Engineering, Kyungpook National University, Daegu, Republic of Korea
| | - Christopher McCallum
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Yihe Wang
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Jason Riordon
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada
| | - Reza Nosrati
- Department of Mechanical & Aerospace Engineering, Monash University, Clayton, VIC, Australia
| | - David Sinton
- Department of Mechanical & Industrial Engineering, University of Toronto, Toronto, ON, Canada.
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Kresch E, Efimenko I, Gonzalez D, Rizk PJ, Ramasamy R. Novel methods to enhance surgical sperm retrieval: a systematic review. Arab J Urol 2021; 19:227-237. [PMID: 34552774 PMCID: PMC8451700 DOI: 10.1080/2090598x.2021.1926752] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 03/04/2021] [Indexed: 01/04/2023] Open
Abstract
Objectives: To explore the use of novel technologies in sperm retrieval in men with azoospermia due to a production defect. Methods: We performed a Preferred Reporting Items for Systemic Reviews and Meta-Analysis (PRISMA)-compliant systemic literature review for manuscripts focussed on novel sperm-retrieval methods. We identified 30 studies suitable for qualitative analysis. Results and Conclusions: We identified multiple new promising technologies, each with its own distinct set of benefits and limitations, to enhance chances of sperm retrieval; these include the use of multiphoton microscopy, Raman spectroscopy, and full-field optical coherence tomography during a microdissection-testicular sperm extraction procedure. ORBEYE and ultrasonography technologies can also serve to better visualise areas of sperm production. Finally, artificial intelligence technology can play a role in the identification of sperm and, perhaps, better-quality sperm for use with assisted reproduction. Abbreviations: AI: artificial intelligence; ANN: artificial neural network; ART: assisted reproductive technology; 3D: three-dimensional; DNN: deep neural networks; FFOCT: full-field optical coherence tomography; H&E: haematoxylin and eosin; ICSI: intracytoplasmic sperm injection; IVF: in vitro fertilisation; MESA: micro-epididymal sperm aspiration; MeSH: Medical Subject Heading; MPM: multiphoton microscopy; (N)OA: (non-)obstructive azoospermia; SCO: Sertoli cell-only syndrome; SRR: sperm retrieval rates; TESA: testicular sperm aspiration; (micro-)TESE: (microdissection-) testicular sperm extraction; (CE)US: (contrast-enhanced) ultrasonography.
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Affiliation(s)
- Eliyahu Kresch
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Iakov Efimenko
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Daniel Gonzalez
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Paul J. Rizk
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Ranjith Ramasamy
- Department of Urology, University of Miami Miller School of Medicine, Miami, FL, USA
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Zaninovic N, Rosenwaks Z. Artificial intelligence in human in vitro fertilization and embryology. Fertil Steril 2021; 114:914-920. [PMID: 33160513 DOI: 10.1016/j.fertnstert.2020.09.157] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 09/23/2020] [Indexed: 02/06/2023]
Abstract
Embryo evaluation and selection embody the aggregate manifestation of the entire in vitro fertilization (IVF) process. It aims to choose the "best" embryos from the larger cohort of fertilized oocytes, the majority of which will be determined to be not viable either as a result of abnormal development or due to chromosomal imbalances. Indeed, it is generally acknowledged that even after embryo selection based on morphology, time-lapse microscopic photography, or embryo biopsy with preimplantation genetic testing, implantation rates in the human are difficult to predict. Our pursuit of enhancing embryo evaluation and selection, as well as increasing live birth rates, will require the adoption of novel technologies. Recently, several artificial intelligence (AI)-based methods have emerged as objective, standardized, and efficient tools for evaluating human embryos. Moreover, AI-based methods can be implemented for other clinical aspects of IVF, such as assessing patient reproductive potential and individualizing gonadotropin stimulation protocols. As AI has the capability to analyze "big" data, the ultimate goal will be to apply AI tools to the analysis of all embryological, clinical, and genetic data in an effort to provide patient-tailored treatments. In this chapter, we present an overview of existing AI technologies in reproductive medicine and envision their potential future applications in the field.
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Affiliation(s)
- Nikica Zaninovic
- The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York.
| | - Zev Rosenwaks
- The Ronald O. Perelman and Claudia Cohen Center for Reproductive Medicine, Weill Cornell Medicine, New York, New York
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Abstract
Intracytoplasmic sperm injection (ICSI) is an important technique in male infertility treatment. Currently, sperm selection for ICSI in human assisted reproductive technology (ART) is subjective, based on a visual assessment by the operator. Therefore, it is desirable to develop methods that can objectively provide an accurate assessment of the shape and size of sperm heads that use low-magnification microscopy available in most standard fertility clinics. Recent studies have shown a correlation between sperm head size and shape and chromosomal abnormalities, and fertilization rate, and various attempts have been made to establish automated computer-based measurement of the sperm head itself. For example, a dictionary-learning technique and a deep-learning-based method have both been developed. Recently, an automatic algorithm was reported that detects sperm head malformations in real time for selection of the best sperm for ICSI. These data suggest that a real-time sperm selection system for use in ICSI is necessary. Moreover, these systems should incorporate inverted microscopes (×400-600 magnification) but not the fluorescence microscopy techniques often used for a dictionary-learning technique and a deep-learning-based method. These advances are expected to improve future success rates of ARTs. In this review, we summarize recent reports on the assessment of sperm head shape, size, and acrosome status in relation to fertility, and propose further improvements that can be made to the ARTs used in infertility treatments.
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Somasundaram D, Nirmala M. Faster region convolutional neural network and semen tracking algorithm for sperm analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105918. [PMID: 33465511 DOI: 10.1016/j.cmpb.2020.105918] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 12/20/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVES Semen analysis is a primary and mandatory procedure to evaluate the infertility during clinical examination. This procedure includes the analysis and classification of normal and abnormal Sperm, selection and efficient tracking of healthy sperm in the sample. Many methods were proposed earlier for the analysis of semen. The fast sperm movement and high dense cluster of sperm is a challenging task for researchers. METHODS The paper proposes a novel Faster Region Convolutional Neural Network (FRCNN) with Elliptic Scanning Algorithm (ESA) for classifying human sperm and a Novel Tail to Head movement algorithm (THMA) for the motility analysis and tracking. This proposed method improves the accuracy of computer assisted semen analysis (CASA). RESULTS The proposed method outperforms and provides better results than existing methods. Method provides better accuracy of 97.37%. Sperm detection and identifying the sperm motility in the group is performed with minimum execution time of 1.12 s. CONCLUSIONS A novel FRCNN with ESA detection algorithm is proposed for the analysis of human sperm classification. This method provides an accuracy of 97.37%. A Tail head movement-based (THMA) algorithm is explained for the motility analysis.
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Affiliation(s)
- Devaraj Somasundaram
- Department of Biomedical Engineering, Sri Shakthi institute of Engineering and Technology, Coimbatore - 641062, Tamilnadu, India.
| | - Madian Nirmala
- Department of Electronics and Communication Engineering, Sri Shakthi institute of Engineering and Technology, Coimbatore-641062, Tamilnadu, India
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Valiuškaitė V, Raudonis V, Maskeliūnas R, Damaševičius R, Krilavičius T. Deep Learning Based Evaluation of Spermatozoid Motility for Artificial Insemination. SENSORS (BASEL, SWITZERLAND) 2020; 21:E72. [PMID: 33374461 PMCID: PMC7795243 DOI: 10.3390/s21010072] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/15/2020] [Accepted: 12/21/2020] [Indexed: 12/15/2022]
Abstract
We propose a deep learning method based on the Region Based Convolutional Neural Networks (R-CNN) architecture for the evaluation of sperm head motility in human semen videos. The neural network performs the segmentation of sperm heads, while the proposed central coordinate tracking algorithm allows us to calculate the movement speed of sperm heads. We have achieved 91.77% (95% CI, 91.11-92.43%) accuracy of sperm head detection on the VISEM (A Multimodal Video Dataset of Human Spermatozoa) sperm sample video dataset. The mean absolute error (MAE) of sperm head vitality prediction was 2.92 (95% CI, 2.46-3.37), while the Pearson correlation between actual and predicted sperm head vitality was 0.969. The results of the experiments presented below will show the applicability of the proposed method to be used in automated artificial insemination workflow.
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Affiliation(s)
- Viktorija Valiuškaitė
- Department of Control Systems, Kaunas University of Technology, 51423 Kaunas, Lithuania; (V.V.); (V.R.)
| | - Vidas Raudonis
- Department of Control Systems, Kaunas University of Technology, 51423 Kaunas, Lithuania; (V.V.); (V.R.)
| | - Rytis Maskeliūnas
- Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania;
| | - Robertas Damaševičius
- Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania;
- Faculty of Applied Mathematics, Silesian University of Technology, 444-100 Gliwice, Poland
| | - Tomas Krilavičius
- Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania;
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Dabbagh SR, Rabbi F, Doğan Z, Yetisen AK, Tasoglu S. Machine learning-enabled multiplexed microfluidic sensors. BIOMICROFLUIDICS 2020; 14:061506. [PMID: 33343782 PMCID: PMC7733540 DOI: 10.1063/5.0025462] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 12/01/2020] [Indexed: 05/02/2023]
Abstract
High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.
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Affiliation(s)
| | - Fazle Rabbi
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey
| | | | - Ali Kemal Yetisen
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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Lesani A, Kazemnejad S, Moghimi Zand M, Azadi M, Jafari H, Mofrad MRK, Nosrati R. Quantification of human sperm concentration using machine learning-based spectrophotometry. Comput Biol Med 2020; 127:104061. [PMID: 33126127 DOI: 10.1016/j.compbiomed.2020.104061] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 10/10/2020] [Accepted: 10/14/2020] [Indexed: 12/17/2022]
Abstract
Spectrophotometry is an indirect non-invasive and quantitative method for specifying materials with unknown contents based on absorption behavior. This paper presents the first application of artificial neural network in spectrophotometry for quantification of human sperm concentration. A well-trained full spectrum neural network (FSNN) model is developed by examining the absorption response of sperm samples from 41 human subjects to different light spectra (wavelength from 390 to 1100 nm). It is shown that this FSNN accurately estimates sperm concentration based on the full absorption spectrum with over 93% prediction accuracy, and provides 100% agreement with clinical assessments in differentiating the samples of healthy donor from patient samples. We suggest the machine learning-based spectrophotometry approach with the trained FSNN model as a rapid, low-cost, and powerful technique to quantify sperm concentration. The performance of this technique is superior to available spectrophotometry methods currently used for semen analysis and will provide novel research and clinical opportunities for tackling male infertility.
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Affiliation(s)
- Ali Lesani
- Small Medical Devices, BioMEMS and LoC Lab, School of Mechanical Engineering, College of Engineering, University of Tehran, Iran
| | - Somaieh Kazemnejad
- Reproductive Biotechnology Research Center, Avicenna Research Institute, ACECR, Tehran, Iran
| | - Mahdi Moghimi Zand
- Small Medical Devices, BioMEMS and LoC Lab, School of Mechanical Engineering, College of Engineering, University of Tehran, Iran.
| | - Mojtaba Azadi
- School of Engineering, San Francisco State University, USA
| | - Hassan Jafari
- Department of Biostatistics and Health Informatics, Institute of Psychology Psychiatry and Neuroscience, King's College London, United Kingdom
| | | | - Reza Nosrati
- Department of Mechanical & Aerospace Engineering, Monash University, Clayton, Victoria, 3800, Australia.
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