1
|
Norton C, Pollard C, Stalker K, Aston K, Jenkins T. Novel bioinformatic analyses of somatic cell contamination in sperm samples. Syst Biol Reprod Med 2024; 70:174-182. [PMID: 38908909 DOI: 10.1080/19396368.2024.2368716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Accepted: 06/11/2024] [Indexed: 06/24/2024]
Abstract
The assessment of epigenetic profiles in sperm is sensitive to somatic cell contamination, which can influence methylation signals at gene promoters. This contamination is particularly problematic in the assessment of DNA methylation in samples with low sperm counts, where fractional amounts of somatic cell DNA can lead to significant shifts in measured methylation state. In this study, a new method of detecting possible somatic cell contamination is proposed through two multi-region bioinformatic models: a traditional differential methylation analysis and a machine learning logistic regression model. These models were trained on publicly available sperm (n = 489) and blood (n = 1029) DNA methylation array data and tested on a contamination set, wherein the sperm of four donors with normal sperm counts were run on a 450k methylation array with four permutations each, including pure blood, half blood and half sperm by DNA concentration, half blood and half sperm by cell count, and pure sperm (n = 16). The DMR and logistic regression model classified the contamination testing set with 100% and 94% accuracy, respectively. These new methods of detecting the effects of somatic cell contamination allow for more accurate differentiation between epigenetic profiles that contain a biological somatic-like shift and those that have somatic-like signatures because of contamination.
Collapse
Affiliation(s)
- Carter Norton
- Department of Cell Biology, Brigham Young University, Provo, UT, USA
| | - Chad Pollard
- Department of Cell Biology, Brigham Young University, Provo, UT, USA
| | - Kelaney Stalker
- Department of Cell Biology, Brigham Young University, Provo, UT, USA
| | - Kenneth Aston
- Department of Surgery, University of Utah, Salt Lake City, UT, USA
| | - Timothy Jenkins
- Department of Cell Biology, Brigham Young University, Provo, UT, USA
| |
Collapse
|
2
|
Chavez-Badiola A, Farías AFS, Mendizabal-Ruiz G, Silvestri G, Griffin DK, Valencia-Murillo R, Drakeley AJ, Cohen J. Use of artificial intelligence embryo selection based on static images to predict first-trimester pregnancy loss. Reprod Biomed Online 2024; 49:103934. [PMID: 38824762 DOI: 10.1016/j.rbmo.2024.103934] [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: 10/18/2023] [Revised: 02/16/2024] [Accepted: 02/27/2024] [Indexed: 06/04/2024]
Abstract
RESEARCH QUESTION Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos? DESIGN In a blind, retrospective study, a cohort of 172 blastocysts from IVF cases with single embryo transfer and a positive biochemical pregnancy test was ranked retrospectively by the artificial intelligence morphometric algorithm ERICA. Making use of static embryo images from a light microscope, each blastocyst was assigned to one of four possible groups (optimal, good, fair or poor), and linear regression was used to correlate the results with the presence or absence of a normal fetal heart beat as an indicator of ongoing pregnancy or spontaneous abortion, respectively. Additional analyses included modelling for recipient age and chromosomal status established by preimplantation genetic testing for aneuploidy (PGT-A). RESULTS Embryos classified as optimal/good had a lower incidence of spontaneous abortion (16.1%) compared with embryos classified as fair/poor (25%; OR = 0.46, P = 0.005). The incidence of spontaneous abortion in chromosomally normal embryos (determined by PGT-A) was 13.3% for optimal/good embryos and 20.0% for fair/poor embryos, although the difference was not significant (P = 0.531). There was a significant association between embryo rank and recipient age (P = 0.018), in that the incidence of spontaneous abortion was unexpectedly lower in older recipients (21.3% for age ≤35 years, 17.9% for age 36-38 years, 16.4% for age ≥39 years; OR = 0.354, P = 0.0181). Overall, these results support correlation between risk of spontaneous abortion and embryo rank as determined by artificial intelligence; classification accuracy was calculated to be 67.4%. CONCLUSIONS This preliminary study suggests that artificial intelligence (ERICA), which was designed as a ranking system to assist with embryo transfer decisions and ploidy prediction, may also be useful to provide information for couples on the risk of spontaneous abortion. Future work will include a larger sample size and karyotyping of miscarried pregnancy tissue.
Collapse
Affiliation(s)
- Alejandro Chavez-Badiola
- University of Kent, School of Biosciences, Canterbury, UK; IVF 2.0 Ltd, London, UK; New Hope Fertility Center, Guadalajara, Mexico; Conceivable Life Sciences, New York, NY, USA
| | | | - Gerardo Mendizabal-Ruiz
- Conceivable Life Sciences, New York, NY, USA; Departamento de Ciencias Computacionales, Universidad de Guadalajara, Guadalajara, Mexico
| | - Giuseppe Silvestri
- University of Kent, School of Biosciences, Canterbury, UK; Conceivable Life Sciences, New York, NY, USA
| | | | | | - Andrew J Drakeley
- IVF 2.0 Ltd, London, UK; Hewitt Fertility Centre, Liverpool Women's NHS Foundation Trust, Liverpool, UK
| | - Jacques Cohen
- IVF 2.0 Ltd, London, UK; Conceivable Life Sciences, New York, NY, USA
| |
Collapse
|
3
|
Gül M, Russo GI, Kandil H, Boitrelle F, Saleh R, Chung E, Kavoussi P, Mostafa T, Shah R, Agarwal A. Male Infertility: New Developments, Current Challenges, and Future Directions. World J Mens Health 2024; 42:502-517. [PMID: 38164030 PMCID: PMC11216957 DOI: 10.5534/wjmh.230232] [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: 08/16/2023] [Accepted: 08/27/2023] [Indexed: 01/03/2024] Open
Abstract
There have been many significant scientific advances in the diagnostics and treatment modalities in the field of male infertility in recent decades. Examples of these include assisted reproductive technologies, sperm selection techniques for intracytoplasmic sperm injection, surgical procedures for sperm retrieval, and novel tests of sperm function. However, there is certainly a need for new developments in this field. In this review, we discuss advances in the management of male infertility, such as seminal oxidative stress testing, sperm DNA fragmentation testing, genetic and epigenetic tests, genetic manipulations, artificial intelligence, personalized medicine, and telemedicine. The role of the reproductive urologist will continue to expand in future years to address different topzics related to diverse questions and controversies of pathophysiology, diagnosis, and therapy of male infertility, training researchers and physicians in medical and scientific research in reproductive urology/andrology, and further development of andrology as an independent specialty.
Collapse
Affiliation(s)
- Murat Gül
- Department of Urology, Selcuk University School of Medicine, Konya, Turkey
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Giorgio Ivan Russo
- Urology Section, University of Catania, Catania, Italy
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Hussein Kandil
- Fakih IVF Fertility Center, Abu Dhabi, UAE
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Florence Boitrelle
- Reproductive Biology, Fertility Preservation, Andrology, CECOS, Poissy Hospital, Poissy, France
- Paris Saclay University, UVSQ, INRAE, BREED, Jouy-en-Josas, France
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Ramadan Saleh
- Department of Dermatology, Venereology and Andrology, Faculty of Medicine, Sohag University, Sohag, Egypt
- Ajyal IVF Center, Ajyal Hospital, Sohag, Egypt
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Eric Chung
- Department of Urology, Princess Alexandra Hospital, University of Queensland, Brisbane, QLD, Australia
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Parviz Kavoussi
- Department of Reproductive Urology, Austin Fertility & Reproductive Medicine/Westlake IVF, Austin, TX, USA
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Taymour Mostafa
- Department of Andrology, Sexology and STIs, Faculty of Medicine, Cairo University, Cairo, Egypt
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Rupin Shah
- Department of Urology, Lilavati Hospital and Research Centre, Mumbai, India
- Well Women's Centre, Sir HN Reliance Foundation Hospital, Mumbai, India
- Global Andrology Forum, Moreland Hills, OH, USA
| | - Ashok Agarwal
- Global Andrology Forum, Moreland Hills, OH, USA
- Cleveland Clinic, Cleveland, OH, USA.
| |
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
Schmeis Arroyo V, Iosa M, Antonucci G, De Bartolo D. Predicting Male Infertility Using Artificial Neural Networks: A Review of the Literature. Healthcare (Basel) 2024; 12:781. [PMID: 38610202 PMCID: PMC11011284 DOI: 10.3390/healthcare12070781] [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: 02/05/2024] [Revised: 03/27/2024] [Accepted: 03/31/2024] [Indexed: 04/14/2024] Open
Abstract
Male infertility is a relevant public health problem, but there is no systematic review of the different machine learning (ML) models and their accuracy so far. The present review aims to comprehensively investigate the use of ML algorithms in predicting male infertility, thus reporting the accuracy of the used models in the prediction of male infertility as a primary outcome. Particular attention will be paid to the use of artificial neural networks (ANNs). A comprehensive literature search was conducted in PubMed, Scopus, and Science Direct between 15 July and 23 October 2023, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We performed a quality assessment of the included studies using the recommended tools suggested for the type of study design adopted. We also made a screening of the Risk of Bias (RoB) associated with the included studies. Thus, 43 relevant publications were included in this review, for a total of 40 different ML models detected. The studies included reported a good quality, even if RoB was not always good for all the types of studies. The included studies reported a median accuracy of 88% in predicting male infertility using ML models. We found only seven studies using ANN models for male infertility prediction, reporting a median accuracy of 84%.
Collapse
Affiliation(s)
- Vivian Schmeis Arroyo
- Department of Psychology, University Sapienza of Rome, 00185 Rome, Italy (M.I.); (G.A.)
| | - Marco Iosa
- Department of Psychology, University Sapienza of Rome, 00185 Rome, Italy (M.I.); (G.A.)
- Santa Lucia Foundation, Scientific Institute for Research, Hospitalization and Health Care (IRCCS), 00179 Rome, Italy
| | - Gabriella Antonucci
- Department of Psychology, University Sapienza of Rome, 00185 Rome, Italy (M.I.); (G.A.)
- Santa Lucia Foundation, Scientific Institute for Research, Hospitalization and Health Care (IRCCS), 00179 Rome, Italy
| | - Daniela De Bartolo
- Santa Lucia Foundation, Scientific Institute for Research, Hospitalization and Health Care (IRCCS), 00179 Rome, Italy
| |
Collapse
|
6
|
Peña FJ, Martín-Cano FE, Becerro-Rey L, Ortega-Ferrusola C, Gaitskell-Phillips G, da Silva-Álvarez E, Gil MC. The future of equine semen analysis. Reprod Fertil Dev 2024; 36:RD23212. [PMID: 38467450 DOI: 10.1071/rd23212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 02/15/2024] [Indexed: 03/13/2024] Open
Abstract
We are currently experiencing a period of rapid advancement in various areas of science and technology. The integration of high throughput 'omics' techniques with advanced biostatistics, and the help of artificial intelligence, is significantly impacting our understanding of sperm biology. These advances will have an appreciable impact on the practice of reproductive medicine in horses. This article provides a brief overview of recent advances in the field of spermatology and how they are changing assessment of sperm quality. This article is written from the authors' perspective, using the stallion as a model. We aim to portray a brief overview of the changes occurring in the assessment of sperm motility and kinematics, advances in flow cytometry, implementation of 'omics' technologies, and the use of artificial intelligence/self-learning in data analysis. We also briefly discuss how some of the advances can be readily available to the practitioner, through the implementation of 'on-farm' devices and telemedicine.
Collapse
Affiliation(s)
- Fernando J Peña
- Laboratory of Equine Reproduction and Equine Spermatology, Veterinary Teaching Hospital, University of Extremadura, Cáceres, Spain
| | - Francisco Eduardo Martín-Cano
- Laboratory of Equine Reproduction and Equine Spermatology, Veterinary Teaching Hospital, University of Extremadura, Cáceres, Spain
| | - Laura Becerro-Rey
- Laboratory of Equine Reproduction and Equine Spermatology, Veterinary Teaching Hospital, University of Extremadura, Cáceres, Spain
| | - Cristina Ortega-Ferrusola
- Laboratory of Equine Reproduction and Equine Spermatology, Veterinary Teaching Hospital, University of Extremadura, Cáceres, Spain
| | - Gemma Gaitskell-Phillips
- Laboratory of Equine Reproduction and Equine Spermatology, Veterinary Teaching Hospital, University of Extremadura, Cáceres, Spain
| | - Eva da Silva-Álvarez
- Laboratory of Equine Reproduction and Equine Spermatology, Veterinary Teaching Hospital, University of Extremadura, Cáceres, Spain
| | - María Cruz Gil
- Laboratory of Equine Reproduction and Equine Spermatology, Veterinary Teaching Hospital, University of Extremadura, Cáceres, Spain
| |
Collapse
|
7
|
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.
Collapse
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.
| |
Collapse
|
8
|
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.
Collapse
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.)
| |
Collapse
|
9
|
Kobayashi H. Potential for artificial intelligence in medicine and its application to male infertility. Reprod Med Biol 2024; 23:e12590. [PMID: 38948339 PMCID: PMC11211808 DOI: 10.1002/rmb2.12590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 05/15/2024] [Accepted: 05/27/2024] [Indexed: 07/02/2024] Open
Abstract
Background The third AI boom, which began in 2010, has been characterized by the rapid evolution and diversification of AI and marked by the development of key technologies such as machine learning and deep learning. AI is revolutionizing the medical field, enhancing diagnostic accuracy, surgical outcomes, and drug production. Methods This review includes explanations of digital transformation (DX), the history of AI, the difference between machine learning and deep learning, recent AI topics, medical AI, and AI research in male infertility. Main Findings Results In research on male infertility, I established an AI-based prediction model for Johnsen scores and an AI predictive model for sperm retrieval in non-obstructive azoospermia, both by no-code AI. Conclusions AI is making constant progress. It would be ideal for physicians to acquire a knowledge of AI and even create AI models. No-code AI tools have revolutionized model creation, allowing individuals to independently handle data preparation and model development. Previously a team effort, this shift empowers users to craft customized AI models solo, offering greater flexibility and control in the model creation process.
Collapse
|
10
|
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.
Collapse
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
| | | |
Collapse
|
11
|
Ferrand T, Boulant J, He C, Chambost J, Jacques C, Pena CA, Hickman C, Reignier A, Fréour T. Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning. Hum Reprod 2023; 38:1918-1926. [PMID: 37581894 PMCID: PMC10546073 DOI: 10.1093/humrep/dead163] [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: 04/14/2022] [Revised: 07/05/2023] [Indexed: 08/16/2023] Open
Abstract
STUDY QUESTION Can machine learning predict the number of oocytes retrieved from controlled ovarian hyperstimulation (COH)? SUMMARY ANSWER Three machine-learning models were successfully trained to predict the number of oocytes retrieved from COH. WHAT IS KNOWN ALREADY A number of previous studies have identified and built predictive models on factors that influence the number of oocytes retrieved during COH. Many of these studies are, however, limited in the fact that they only consider a small number of variables in isolation. STUDY DESIGN, SIZE, DURATION This study was a retrospective analysis of a dataset of 11,286 cycles performed at a single centre in France between 2009 and 2020 with the aim of building a predictive model for the number of oocytes retrieved from ovarian stimulation. The analysis was carried out by a data analysis team external to the centre using the Substra framework. The Substra framework enabled the data analysis team to send computer code to run securely on the centre's on-premises server. In this way, a high level of data security was achieved as the data analysis team did not have direct access to the data, nor did the data leave the centre at any point during the study. PARTICIPANTS/MATERIALS, SETTING, METHODS The Light Gradient Boosting Machine algorithm was used to produce three predictive models: one that directly predicted the number of oocytes retrieved and two that predicted which of a set of bins provided by two clinicians the number of oocytes retrieved fell into. The resulting models were evaluated on a held-out test set and compared to linear and logistic regression baselines. In addition, the models themselves were analysed to identify the parameters that had the biggest impact on their predictions. MAIN RESULTS AND THE ROLE OF CHANCE On average, the model that directly predicted the number of oocytes retrieved deviated from the ground truth by 4.21 oocytes. The model that predicted the first clinician's bins deviated by 0.73 bins whereas the model for the second clinician deviated by 0.62 bins. For all models, performance was best within the first and third quartiles of the target variable, with the model underpredicting extreme values of the target variable (no oocytes and large numbers of oocytes retrieved). Nevertheless, the erroneous predictions made for these extreme cases were still within the vicinity of the true value. Overall, all three models agreed on the importance of each feature which was estimated using Shapley Additive Explanation (SHAP) values. The feature with the highest mean absolute SHAP value (and thus the highest importance) was the antral follicle count, followed by basal AMH and FSH. Of the other hormonal features, basal TSH, LH, and testosterone levels were similarly important and baseline LH was the least important. The treatment characteristic with the highest SHAP value was the initial dose of gonadotropins. LIMITATIONS, REASONS FOR CAUTION The models produced in this study were trained on a cohort from a single centre. They should thus not be used in clinical practice until trained and evaluated on a larger cohort more representative of the general population. WIDER IMPLICATIONS OF FINDINGS These predictive models for the number of oocytes retrieved from COH may be useful in clinical practice, assisting clinicians in optimizing COH protocols for individual patients. Our work also demonstrates the promise of using the Substra framework for allowing external researchers to provide clinically relevant insights on sensitive fertility data in a fully secure, trustworthy manner and opens a number of exciting avenues for accelerating future research. STUDY FUNDING/COMPETING INTEREST(S) This study was funded by the French Public Bank of Investment as part of the Healthchain Consortium. T.Fe., C.He., J.C., C.J., C.-A.P., and C.Hi. are employed by Apricity. C.Hi. has received consulting fees and honoraria from Vitrolife, Merck Serono, Ferring, Cooper Surgical, Dibimed, Apricity, and Fairtility and travel support from Fairtility and Vitrolife, participates on an advisory board for Merck Serono, was the founder and organizer of the AI Fertility conference, has stock in Aria Fertility, TMRW, Fairtility, Apricity, and IVF Professionals, and received free equipment from Planar in exchange for first user feedback. C.J. has received a grant from BPI. J.C. has also received a grant from BPI, is a member of the Merck AI advisory board, and is a board member of Labelia Labs. C.He has a contract for medical writing of this manuscript by CHU Nantes and has received travel support from Apricity. A.R. haș received honoraria from Ferring and Organon. T.Fe. has received a grant from BPI. TRIAL REGISTRATION NUMBER N/A.
Collapse
Affiliation(s)
| | | | - Chloe He
- AI Team, Apricity, London, UK
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK
- Department of Computer Science, University College London, London, UK
| | | | | | | | - Cristina Hickman
- AI Team, Apricity, London, UK
- Institute of Reproductive and Developmental Biology, Imperial College London, London, UK
| | | | - Thomas Fréour
- Centre Hospitalier Universitaire de Nantes, Nantes, France
- Department of Reproductive Medicine, Dexeus University Hospital, Barcelona, Spain
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Haugen TB, Witczak O, Hicks SA, Björndahl L, Andersen JM, Riegler MA. Sperm motility assessed by deep convolutional neural networks into WHO categories. Sci Rep 2023; 13:14777. [PMID: 37679484 PMCID: PMC10484948 DOI: 10.1038/s41598-023-41871-2] [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: 06/29/2023] [Accepted: 09/01/2023] [Indexed: 09/09/2023] Open
Abstract
Semen analysis is central in infertility investigation. Manual assessment of sperm motility according to the WHO recommendations is the golden standard, and extensive training is a requirement for accurate and reproducible results. Deep convolutional neural networks (DCNN) are especially suitable for image classification. In this study, we evaluated the performance of the DCNN ResNet-50 in predicting the proportion of sperm in the WHO motility categories. Two models were evaluated using tenfold cross-validation with 65 video recordings of wet semen preparations from an external quality assessment programme for semen analysis. The corresponding manually assessed data was obtained from several of the reference laboratories, and the mean values were used for training of the DCNN models. One model was trained to predict the three categories progressive motility, non-progressive motility, and immotile spermatozoa. Another model was used in predicting four categories, where progressive motility was differentiated into rapid and slow. The resulting average mean absolute error (MAE) was 0.05 and 0.07, and the average ZeroR baseline was 0.09 and 0.10 for the three-category and the four-category model, respectively. Manual and DCNN-predicted motility was compared by Pearson's correlation coefficient and by difference plots. The strongest correlation between the mean manually assessed values and DCNN-predicted motility was observed for % progressively motile spermatozoa (Pearson's r = 0.88, p < 0.001) and % immotile spermatozoa (r = 0.89, p < 0.001). For rapid progressive motility, the correlation was moderate (Pearson's r = 0.673, p < 0.001). The median difference between manual and predicted progressive motility was 0 and 2 for immotile spermatozoa. The largest bias was observed at high and low percentages of progressive and immotile spermatozoa. The DCNN-predicted value was within the range of the interlaboratory variation of the results for most of the samples. In conclusion, DCNN models were able to predict the proportion of spermatozoa into the WHO motility categories with significantly lower error than the baseline. The best correlation between the manual and the DCNN-predicted motility values was found for the categories progressive and immotile. Of note, there was considerable variation between the mean motility values obtained for each category by the reference laboratories, especially for rapid progressive motility, which impacts the training of the DCNN models.
Collapse
Affiliation(s)
- Trine B Haugen
- Department of Life Sciences and Health, OsloMet - Oslo Metropolitan University, Oslo, Norway.
| | - Oliwia Witczak
- Department of Life Sciences and Health, OsloMet - Oslo Metropolitan University, Oslo, Norway
| | - Steven A Hicks
- Simula Metropolitan Center for Digital Engineering, Oslo, Norway
| | - Lars Björndahl
- ANOVA, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | - Jorunn M Andersen
- Department of Life Sciences and Health, OsloMet - Oslo Metropolitan University, Oslo, Norway
| | | |
Collapse
|
14
|
GhoshRoy D, Alvi PA, Santosh KC. AI Tools for Assessing Human Fertility Using Risk Factors: A State-of-the-Art Review. J Med Syst 2023; 47:91. [PMID: 37610455 DOI: 10.1007/s10916-023-01983-8] [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: 07/15/2022] [Accepted: 08/02/2023] [Indexed: 08/24/2023]
Abstract
Infertility has massively disrupted social and marital life, resulting in stressful emotional well-being. Early diagnosis is the utmost need for faster adaption to respond to these changes, which makes possible via AI tools. Our main objective is to comprehend the role of AI in fertility detection since we have primarily worked to find biomarkers and related risk factors associated with infertility. This paper aims to vividly analyse the role of AI as an effective method in screening, predicting for infertility and related risk factors. Three scientific repositories: PubMed, Web of Science, and Scopus, are used to gather relevant articles via technical terms: (human infertility OR human fertility) AND risk factors AND (machine learning OR artificial intelligence OR intelligent system). In this way, we systematically reviewed 42 articles and performed a meta-analysis. The significant findings and recommendations are discussed. These include the rising importance of data augmentation, feature extraction, explainability, and the need to revisit the meaning of an effective system for fertility analysis. Additionally, the paper outlines various mitigation actions that can be employed to tackle infertility and its related risk factors. These insights contribute to a better understanding of the role of AI in fertility analysis and the potential for improving reproductive health outcomes.
Collapse
Affiliation(s)
- Debasmita GhoshRoy
- School of Automation, Banasthali Vidyapith, 304022, Rajasthan, India
- Applied AI Research Lab, Vermillion, SD, 57069, USA
| | - P A Alvi
- Department of Physics, Banasthali Vidyapith, 304022, Rajasthan, India
| | - K C Santosh
- Department of Computer Science, University of South Dakota, Vermillion, SD, 57069, USA.
- Applied AI Research Lab, Vermillion, SD, 57069, USA.
| |
Collapse
|
15
|
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.
Collapse
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
| |
Collapse
|
16
|
Zhu R, Cui Y, Huang J, Hou E, Zhao J, Zhou Z, Li H. YOLOv5s-SA: Light-Weighted and Improved YOLOv5s for Sperm Detection. Diagnostics (Basel) 2023; 13:diagnostics13061100. [PMID: 36980408 PMCID: PMC10047898 DOI: 10.3390/diagnostics13061100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 03/07/2023] [Accepted: 03/11/2023] [Indexed: 03/17/2023] Open
Abstract
Sperm detection performance is particularly critical for sperm motility tracking. However, there are a large number of non-sperm objects, sperm occlusion and poorly detailed texture features in semen images, which directly affect the accuracy of sperm detection. To solve the problem of false detection and missed detection in sperm detection, a multi-sperm target detection model, Yolov5s-SA, with an SA attention mechanism is proposed based on the YOLOv5s algorithm. Firstly, a depthwise, separable convolution structure is used to replace the partial convolution of the backbone network, which can ensure stable precision and reduce the number of model parameters. Secondly, a new multi-scale feature fusion module is designed to enhance the perception of feature information to supplement the positional information and high-resolution of the deep feature map. Finally, the SA attention mechanism is integrated into the neck network before the output of the feature map to enhance the correlation between the feature map channels and improve the fine-grained feature fusion ability of YOLOv5s. Experimental results show that compared with various YOLO algorithms, the proposed algorithm improves the detection accuracy and speed to a certain extent. Compared with the YOLOv3, YOLOv3-spp, YOLOv5s and YOLOv5m models, the average accuracy increases by 18.1%, 15.2%, 6.9% and 1.9%, respectively. It can effectively reduce the missed detection of occluded sperm and achieve lightweight and efficient multi-sperm target detection.
Collapse
Affiliation(s)
- Ronghua Zhu
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Yansong Cui
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
- Correspondence:
| | - Jianming Huang
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Enyu Hou
- SAS Medical Technology (Beijing) Co., Ltd., Changping District, Beijing 102200, China
| | - Jiayu Zhao
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Zhilin Zhou
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Hao Li
- School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
| |
Collapse
|
17
|
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.
Collapse
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
| |
Collapse
|
18
|
Goh VH, As'Ari MAB, Ismail LHB. 3D Convolutional Neural Networks for Sperm Motility Prediction. 2022 2ND INTERNATIONAL CONFERENCE ON INTELLIGENT CYBERNETICS TECHNOLOGY & APPLICATIONS (ICICYTA) 2022. [DOI: 10.1109/icicyta57421.2022.10037950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Affiliation(s)
- Voon Hueh Goh
- Universiti Teknologi Malaysia,School of Biomedical Engineering and Health Science Faculty of Engineering,Johor,Malaysia
| | - Muhammad Amir Bin As'Ari
- Universiti Teknologi Malaysia,Sport Innovation & Technology Centre (SITC) Faculty of Engineering,Johor,Malaysia
| | - Lukman Hakim Bin Ismail
- Universiti Teknologi Malaysia,School of Biomedical Engineering and Health Science Faculty of Engineering,Johor,Malaysia
| |
Collapse
|
19
|
Medenica S, Zivanovic D, Batkoska L, Marinelli S, Basile G, Perino A, Cucinella G, Gullo G, Zaami S. The Future Is Coming: Artificial Intelligence in the Treatment of Infertility Could Improve Assisted Reproduction Outcomes-The Value of Regulatory Frameworks. Diagnostics (Basel) 2022; 12:diagnostics12122979. [PMID: 36552986 PMCID: PMC9777042 DOI: 10.3390/diagnostics12122979] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 11/22/2022] [Accepted: 11/25/2022] [Indexed: 11/29/2022] Open
Abstract
Infertility is a global health issue affecting women and men of reproductive age with increasing incidence worldwide, in part due to greater awareness and better diagnosis. Assisted reproduction technologies (ART) are considered the ultimate step in the treatment of infertility. Recently, artificial intelligence (AI) has been progressively used in the many fields of medicine, integrating knowledge and computer science through machine learning algorithms. AI has the potential to improve infertility diagnosis and ART outcomes estimated as pregnancy and/or live birth rate, especially with recurrent ART failure. A broad-ranging review has been conducted, focusing on clinical AI applications up until September 2022, which could be estimated in terms of possible applications, such as ultrasound monitoring of folliculogenesis, endometrial receptivity, embryo selection based on quality and viability, and prediction of post implantation embryo development, in order to eliminate potential contributing risk factors. Oocyte morphology assessment is highly relevant in terms of successful fertilization rate, as well as during oocyte freezing for fertility preservation, and substantially valuable in oocyte donation cycles. AI has great implications in the assessment of male infertility, with computerised semen analysis systems already in use and a broad spectrum of possible AI-based applications in environmental and lifestyle evaluation to predict semen quality. In addition, considerable progress has been made in terms of harnessing AI in cases of idiopathic infertility, to improve the stratification of infertile/fertile couples based on their biological and clinical signatures. With AI as a very powerful tool of the future, our review is meant to summarise current AI applications and investigations in contemporary reproduction medicine, mainly focusing on the nonsurgical aspects of it; in addition, the authors have briefly explored the frames of reference and guiding principles for the definition and implementation of legal, regulatory, and ethical standards for AI in healthcare.
Collapse
Affiliation(s)
- Sanja Medenica
- Department of Endocrinology, Internal Medicine Clinic, Clinical Center of Montenegro, School of Medicine, University of Montenegro, 81000 Podgorica, Montenegro
| | - Dusan Zivanovic
- Clinic of Endocrinology, Diabetes and Metabolic Disorders, University Clinical Center of Serbia, 11000 Belgrade, Serbia
| | - Ljubica Batkoska
- Medical Faculty, Ss. Cyril and Methodius University of Skopje, 1000 Skopje, North Macedonia
| | | | | | - Antonio Perino
- Department of Obstetrics and Gynecology, Villa Sofia Cervello Hospital, IVF UNIT, University of Palermo, 90146 Palermo, Italy
| | - Gaspare Cucinella
- Department of Obstetrics and Gynecology, Villa Sofia Cervello Hospital, IVF UNIT, University of Palermo, 90146 Palermo, Italy
| | - Giuseppe Gullo
- Department of Obstetrics and Gynecology, Villa Sofia Cervello Hospital, IVF UNIT, University of Palermo, 90146 Palermo, Italy
- Correspondence:
| | - Simona Zaami
- Department of Anatomical, Histological, Forensic and Orthopedic Sciences, “Sapienza” University of Rome, 00161 Rome, Italy
| |
Collapse
|
20
|
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.
Collapse
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
| | | |
Collapse
|
21
|
Barratt CLR, Wang C, Baldi E, Toskin I, Kiarie J, Lamb DJ. What advances may the future bring to the diagnosis, treatment, and care of male sexual and reproductive health? Fertil Steril 2022; 117:258-267. [PMID: 35125173 PMCID: PMC8877074 DOI: 10.1016/j.fertnstert.2021.12.013] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 12/15/2021] [Accepted: 12/15/2021] [Indexed: 12/11/2022]
Abstract
Over the past 40 years, since the publication of the original WHO Laboratory Manual for the Examination and Processing of Human Semen, the laboratory methods used to evaluate semen markedly changed and benefited from improved precision and accuracy, as well as the development of new tests and improved, standardized methodologies. Herein, we present the impact of the changes put forth in the sixth edition together with our views of evolving technologies that may change the methods used for the routine semen analysis, up-and-coming areas for the development of new procedures, and diagnostic approaches that will help to extend the often-descriptive interpretations of several commonly performed semen tests that promise to provide etiologies for the abnormal semen parameters observed. As we look toward the publication of the seventh edition of the manual in approximately 10 years, we describe potential advances that could markedly impact the field of andrology in the future.
Collapse
Affiliation(s)
- Christopher L R Barratt
- Division of Systems Medicine, University of Dundee Medical School, Ninewells Hospital, Dundee, Scotland.
| | - Christina Wang
- Clinical and Translational Science Institute, The Lundquist Institute at Harbor-UCLA Medical Center, Torrance, California
| | - Elisabetta Baldi
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Igor Toskin
- Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland
| | - James Kiarie
- Department of Sexual and Reproductive Health and Research, World Health Organization, Geneva, Switzerland
| | - Dolores J Lamb
- The James Buchanan Brady Foundation Department of Urology, Center for Reproductive Genomics and Englander Institute for Personalized Medicine, Weill Cornell Medical College, New York, New York
| |
Collapse
|
22
|
Sato T, Kishi H, Murakata S, Hayashi Y, Hattori T, Nakazawa S, Mori Y, Hidaka M, Kasahara Y, Kusuhara A, Hosoya K, Hayashi H, Okamoto A. A new deep-learning model using YOLOv3 to support sperm selection during intracytoplasmic sperm injection procedure. Reprod Med Biol 2022; 21:e12454. [PMID: 35414764 PMCID: PMC8979154 DOI: 10.1002/rmb2.12454] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/13/2022] [Accepted: 03/16/2022] [Indexed: 11/10/2022] Open
Abstract
Purpose To create and evaluate a machine-learning model for YOLOv3 that can simultaneously perform morphological evaluation and tracking in a short time, which can be adapted to video data under an inverted microscope. Methods Japanese patients who underwent intracytoplasmic sperm injection at the Jikei University School of Medicine and Keiai Reproductive and Endosurgical Clinic from January 2019 to March 2020 were included. An AI model that simultaneously performs morphological assessment and tracking was created and its performance was evaluated. Results For morphological assessment, the sensitivity and positive predictive value (PPV) of this model for abnormal sperm were 0.881 and 0.853, respectively. The sensitivity and PPV for normal sperm were 0.794 and 0.689, respectively. For tracking performance, among the 51 objects, 40 (78.4%) were mostly tracked, 11 (21.6%) were partially tracked, and 0 (0%) were mostly lost. Conclusions This study showed that evaluating sperm morphology while tracking in a single model is possible by training YOLO v3. This model could acquire time-series data of one sperm, which will assist in acquiring and annotating sperm image data.
Collapse
Affiliation(s)
- Takuma Sato
- Department of Obstetrics and GynecologyThe Jikei University School of MedicineTokyoJapan
| | - Hiroshi Kishi
- Department of Obstetrics and GynecologyThe Jikei University School of MedicineTokyoJapan
| | - Saori Murakata
- Department of Obstetrics and GynecologyThe Jikei University School of MedicineTokyoJapan
| | - Yuki Hayashi
- Department of Obstetrics and GynecologyThe Jikei University School of MedicineTokyoJapan
| | | | | | - Yusuke Mori
- Department of Obstetrics and GynecologyThe Jikei University School of MedicineTokyoJapan
| | - Miwa Hidaka
- Department of Obstetrics and GynecologyThe Jikei University School of MedicineTokyoJapan
| | - Yuta Kasahara
- Department of Obstetrics and GynecologyThe Jikei University School of MedicineTokyoJapan
| | - Atsuko Kusuhara
- Department of Obstetrics and GynecologyThe Jikei University School of MedicineTokyoJapan
| | - Kayo Hosoya
- Keiai Reproductive and Endosurgical ClinicSaitamaJapan
| | | | - Aikou Okamoto
- Department of Obstetrics and GynecologyThe Jikei University School of MedicineTokyoJapan
| |
Collapse
|
23
|
Punjani N, Kang C, Lee RK, Goldstein M, Li PS. Technological Advancements in Male Infertility Microsurgery. J Clin Med 2021; 10:jcm10184259. [PMID: 34575370 PMCID: PMC8471566 DOI: 10.3390/jcm10184259] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/12/2021] [Accepted: 09/15/2021] [Indexed: 11/16/2022] Open
Abstract
There have been significant advancements in male infertility microsurgery over time, and there continues to be significant promise for new and emerging techniques, technologies, and methodologies. In this review, we discuss the history of male infertility and the evolution of microsurgery, the essential role of education and training in male infertility microsurgery, and new technologies in this space. We also review the potentially important role of artificial intelligence (AI) in male infertility and microsurgery.
Collapse
Affiliation(s)
- Nahid Punjani
- Center for Male Reproductive Medicine and Microsurgery, Cornell Institute for Reproductive Medicine, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (N.P.); (C.K.); (M.G.)
- Department of Urology, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA;
| | - Caroline Kang
- Center for Male Reproductive Medicine and Microsurgery, Cornell Institute for Reproductive Medicine, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (N.P.); (C.K.); (M.G.)
- Department of Urology, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA;
| | - Richard K. Lee
- Department of Urology, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA;
| | - Marc Goldstein
- Center for Male Reproductive Medicine and Microsurgery, Cornell Institute for Reproductive Medicine, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (N.P.); (C.K.); (M.G.)
- Department of Urology, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA;
| | - Philip S. Li
- Center for Male Reproductive Medicine and Microsurgery, Cornell Institute for Reproductive Medicine, Weill Cornell Medicine, Cornell University, New York, NY 10065, USA; (N.P.); (C.K.); (M.G.)
- Department of Urology, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA;
- Correspondence:
| |
Collapse
|
24
|
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.
Collapse
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
| |
Collapse
|
25
|
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.
Collapse
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.
| |
Collapse
|
26
|
Abstract
PURPOSE OF REVIEW Over the last decade, major advancements in artificial intelligence technology have emerged and revolutionized the extent to which physicians are able to personalize treatment modalities and care for their patients. Artificial intelligence technology aimed at mimicking/simulating human mental processes, such as deep learning artificial neural networks (ANNs), are composed of a collection of individual units known as 'artificial neurons'. These 'neurons', when arranged and interconnected in complex architectural layers, are capable of analyzing the most complex patterns. The aim of this systematic review is to give a comprehensive summary of the contemporary applications of deep learning ANNs in urological medicine. RECENT FINDINGS Fifty-five articles were included in this systematic review and each article was assigned an 'intermediate' score based on its overall quality. Of these 55 articles, nine studies were prospective, but no nonrandomized control trials were identified. SUMMARY In urological medicine, the application of novel artificial intelligence technologies, particularly ANNs, have been considered to be a promising step in improving physicians' diagnostic capabilities, especially with regards to predicting the aggressiveness and recurrence of various disorders. For benign urological disorders, for example, the use of highly predictive and reliable algorithms could be helpful for the improving diagnoses of male infertility, urinary tract infections, and pediatric malformations. In addition, articles with anecdotal experiences shed light on the potential of artificial intelligence-assisted surgeries, such as with the aid of virtual reality or augmented reality.
Collapse
|
27
|
Onofre J, Geenen L, Cox A, Van Der Auwera I, Willendrup F, Andersen E, Campo R, Dhont N, Ombelet W. Simplified sperm testing devices: a possible tool to overcome lack of accessibility and inconsistency in male factor infertility diagnosis. An opportunity for low- and middle- income countries. Facts Views Vis Obgyn 2021; 13:79-93. [PMID: 33889864 PMCID: PMC8051200 DOI: 10.52054/fvvo.13.1.011] [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] [Indexed: 11/08/2022] Open
Abstract
Background Manual semen assessment (MSA) is a key component in a male’s fertility assessment. Clinicians rely on it to make diagnostic and treatment decisions. When performed manually, this routine laboratory test is prone to variability due to human intervention which can lead to misdiagnosis and consequently over- or under- treatment. For standardisation, continuous training, quality control (QC) programs and pricy Computer-Assisted Sperm Analysis (CASA) systems have been proposed, yet, without resolving intra- and inter-laboratory variability. In response, promising simplified sperm testing devices, able to provide cost-effective point-of-care male infertility diagnosis are prospected as a plausible solution to resolve variability and increase access to sperm testing. Materials and methods A throughout literature research for semen testing, sperm analysis, smart-phone assisted semen analysis, ‘at-home’ semen testing, male infertility, infertility in developing countries, infertility in low- and middle-income countries (LMIC) and quantitative sperm analysis was performed. A total of 14 articles, specific to ‘at-home’ simplified sperm assessment, were included to treat the core subject. Results Continuous training and consistent QC, are sine qua none conditions to achieve accurate and comparable MSA. Compliance does not rule-out variability, nevertheless. Emerging simplified sperm assessment devices are an actual alternative to resolve the lack of standardisation and accessibility to sperm analysis. YO ® , SEEM ® , and ExSeed ® are commercially available, user-friendly smartphone-based devices which can accurately measure volume, sperm concentration (millions/ml) and total motile sperm count. More broadly, by cost-effectiveness, availability, accuracy and convenient application, these devices could effectively select patients for first-line artificial reproduction treatments such as intrauterine insemination. Conclusions Accuracy and cost-effectiveness make smart-phone based sperm testing devices a practical and realistic solution to overcome variability in MSA. Importantly, these tools represent an actual opportunity to standardise and improve male subfertility diagnosis and treatment, especially in LMIC. However, before clinical application is possible, guidelines, further testing with special attention on accuracy in washed sperm, availability, cost-benefit and reliability are required.
Collapse
Affiliation(s)
- J Onofre
- Genk Institute for Fertility Technology, Genk, Belgium.,Department of Obstetrics, Gynaecology and Infertility, Ziekenhuis Oost Limburg, Genk, Belgium
| | - L Geenen
- University of Hasselt, Faculty of Medicine and Life Sciences, Diepenbeek, Belgium
| | - A Cox
- Department of Obstetrics, Gynaecology and Infertility, Ziekenhuis Oost Limburg, Genk, Belgium
| | - I Van Der Auwera
- Department of Obstetrics, Gynaecology and Infertility, Ziekenhuis Oost Limburg, Genk, Belgium
| | | | | | - R Campo
- Genk Institute for Fertility Technology, Genk, Belgium.,Department of Obstetrics, Gynaecology and Infertility, Ziekenhuis Oost Limburg, Genk, Belgium
| | - N Dhont
- Genk Institute for Fertility Technology, Genk, Belgium.,Department of Obstetrics, Gynaecology and Infertility, Ziekenhuis Oost Limburg, Genk, Belgium
| | - W Ombelet
- Genk Institute for Fertility Technology, Genk, Belgium.,Department of Obstetrics, Gynaecology and Infertility, Ziekenhuis Oost Limburg, Genk, Belgium
| |
Collapse
|
28
|
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.
Collapse
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
| |
Collapse
|
29
|
|
30
|
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.
Collapse
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;
| |
Collapse
|