With Endoclassify, we transform science and technology into allies to optimize endometrial assessment through transvaginal ultrasound. Enhance your ART with the precision and reliability of Artificial Intelligence, utilizing a novel grading system that determines the receptivity of the endometrium.
The First and Only AI Software for Measuring Endometrial Receptivity in IVF Success. The First and Only AI Software for Measuring Endometrial Receptivity in IVF Success /n We are thrilled to introduce Endoclassify, the first and only AI-powered software designed to measure endometrial receptivity in In Vitro Fertilization (IVF) processes. Based on the study “Artificial Intelligence Model Utilizing Endometrial Analysis to Contribute as a Predictor of Assisted Reproductive Technology Success”, Endoclassify offers unparalleled insights by utilizing advanced endometrial tissue analysis to predict the success of assisted reproductive technologies./nAs the only solution of its kind, Endoclassify revolutionizes fertility treatment by providing specialists with a powerful tool to precisely assess endometrial receptivity, a critical factor in IVF success. This innovation marks a new era in personalized, data-driven reproductive healthcare./nEndoclassify is a groundbreaking milestone in assisted reproduction, bringing cutting-edge technology to improve fertility outcomes for patients seeking to conceive through assisted reproductive techniques.
Read on LinkedIn
The endometrium is a critical component of human fertility, serving as the site for embryo implantation and supporting early pregnancy. Its functions include providing a receptive environment for implantation, responding to hormonal regulation throughout the menstrual cycle, and supplying essential nutrients and oxygen to the developing embryo. Various factors influence endometrial fertility, including adequate thickness, proper receptivity during the implantation window, and the absence of chronic inflammation. Several endometrial abnormalities, such as Asherman's syndrome, endometrial polyps, and endometriosis, can impair implantation and contribute to infertility. Diagnostic tools, including transvaginal ultrasound, endometrial biopsy, and hysteroscopy, aid in assessing endometrial health and identifying abnormalities. Treatment approaches, such as hormonal therapy and surgical interventions, help optimize endometrial conditions for successful pregnancy. Innovations in AI-driven diagnostic solutions, such as Endoclassify, enhance clinicians' ability to evaluate endometrial receptivity with precision, improving fertility outcomes. Understanding the complexities of endometrial health is essential for advancing infertility treatments and improving reproductive success rates.
Read on LinkedIn
Diagnosing human infertility involves a comprehensive evaluation of both partners to identify underlying causes and determine the most appropriate treatment approach. The diagnostic process begins with an initial consultation, including a detailed medical history, physical examination, and semen analysis. Female infertility evaluation includes ovulation testing, hormonal assessments, imaging studies, and, if necessary, laparoscopy to identify structural abnormalities. Male infertility evaluation involves semen analysis, hormonal testing, physical examination, and scrotal ultrasound to detect potential issues affecting sperm production and function. In cases of unexplained infertility, procedures such as intrauterine insemination (IUI) and in vitro fertilization (IVF) help assess fertilization success. Additional tests, including postcoital testing, antisperm antibody detection, and sperm DNA fragmentation analysis, provide further insights into reproductive health. A thorough diagnosis enables personalized treatment strategies, ranging from low-complexity interventions like ovulation induction and IUI to high-complexity assisted reproductive technologies (ART) such as IVF. Identifying the root causes of infertility ensures tailored and effective management, improving the chances of successful conception.
Read on LinkedIn
Human infertility is a global health concern that affects millions of individuals and couples, impacting their emotional well-being, social relationships, economic stability, and overall reproductive health. The psychological effects of infertility include grief, anxiety, depression, and relationship strain, often leading to a loss of identity and self-worth. Social and cultural factors, such as stigma, community pressure, and traditional expectations surrounding childbearing, further exacerbate the challenges faced by individuals experiencing infertility. Economically, the high cost of fertility treatments, lost productivity, and expenses associated with adoption or surrogacy make family-building inaccessible for many. Additionally, infertility is closely linked to reproductive health issues, including sexually transmitted infections (STIs), polycystic ovary syndrome (PCOS), and endometriosis, which can further complicate conception. On a broader scale, infertility affects population dynamics, particularly in countries experiencing declining birth rates. Addressing infertility as a public health priority requires improving access to fertility treatments, comprehensive reproductive healthcare, and support systems that cater to diverse family structures. Recognizing the evolving nature of family composition, it is essential to provide inclusive reproductive healthcare solutions that respect and support the aspirations of all individuals and couples seeking to build families.
Read on LinkedIn
Artificial intelligence (AI) is revolutionizing the diagnosis of human infertility by improving accuracy, efficiency, and personalized care. AI-powered algorithms analyze extensive datasets, including medical histories, laboratory results, and imaging studies, to identify patterns and predict potential causes of infertility with greater precision. Key applications of AI in infertility diagnosis include predictive modeling, image analysis, natural language processing (NLP), and machine learning, all of which enhance decision-making and optimize fertility treatment strategies. AI-driven diagnostic tools such as semen analysis, ovulation prediction, and embryo selection are transforming assisted reproductive technologies (ART), improving the chances of successful conception. However, challenges such as data quality, regulatory approval, clinical validation, and ethical considerations must be addressed to ensure safe and effective AI deployment in reproductive healthcare. The future of AI in infertility diagnosis includes seamless integration with electronic health records (EHRs), personalized treatment planning, and expanded applications in predicting and preventing infertility-related complications. As AI continues to evolve, it holds the potential to enhance reproductive healthcare, making infertility diagnosis and treatment more accessible, precise, and patient-centered.
Read on LinkedIn
We are excited to announce that EndoClassify, our cutting-edge AI-driven solution for endometrial receptivity analysis, is coming soon! This innovative technology is designed to seamlessly integrate into clinical workflows, empowering practitioners to make informed decisions while enhancing In Vitro Fertilization (IVF) success rates.Utilizing advanced artificial intelligence, including convolutional neural networks (Attention U-Net for segmentation and GoogleNet Inception for classification), EndoClassify achieves 95% accuracy in predicting endometrial receptivity. The technology seamlessly integrates into clinical workflows through two flexible deployment options: a secure API for direct system integration and a standalone mobile app for instant analysis via smartphone-captured ultrasound images. Beyond receptivity prediction, EndoClassify ensures data security by storing prediction results with traceability features, supporting continuous improvement in patient care. AI-driven innovations like EndoClassify address broader societal challenges, including declining birth rates and aging populations, by optimizing fertility treatments and advancing reproductive healthcare. As EndoClassify prepares for its official launch, clinics and healthcare providers worldwide can leverage this cutting-edge technology to enhance IVF success rates and transform patient outcomes.
Read on LinkedInThe First and Only AI Software for Measuring Endometrial Receptivity in IVF Success. The First and Only AI Software for Measuring Endometrial Receptivity in IVF Success /n We are thrilled to introduce Endoclassify, the first and only AI-powered software designed to measure endometrial receptivity in In Vitro Fertilization (IVF) processes. Based on the study “Artificial Intelligence Model Utilizing Endometrial Analysis to Contribute as a Predictor of Assisted Reproductive Technology Success”, Endoclassify offers unparalleled insights by utilizing advanced endometrial tissue analysis to predict the success of assisted reproductive technologies./nAs the only solution of its kind, Endoclassify revolutionizes fertility treatment by providing specialists with a powerful tool to precisely assess endometrial receptivity, a critical factor in IVF success. This innovation marks a new era in personalized, data-driven reproductive healthcare./nEndoclassify is a groundbreaking milestone in assisted reproduction, bringing cutting-edge technology to improve fertility outcomes for patients seeking to conceive through assisted reproductive techniques.
Read on LinkedInThe endometrium is a critical component of human fertility, serving as the site for embryo implantation and supporting early pregnancy. Its functions include providing a receptive environment for implantation, responding to hormonal regulation throughout the menstrual cycle, and supplying essential nutrients and oxygen to the developing embryo. Various factors influence endometrial fertility, including adequate thickness, proper receptivity during the implantation window, and the absence of chronic inflammation. Several endometrial abnormalities, such as Asherman's syndrome, endometrial polyps, and endometriosis, can impair implantation and contribute to infertility. Diagnostic tools, including transvaginal ultrasound, endometrial biopsy, and hysteroscopy, aid in assessing endometrial health and identifying abnormalities. Treatment approaches, such as hormonal therapy and surgical interventions, help optimize endometrial conditions for successful pregnancy. Innovations in AI-driven diagnostic solutions, such as Endoclassify, enhance clinicians' ability to evaluate endometrial receptivity with precision, improving fertility outcomes. Understanding the complexities of endometrial health is essential for advancing infertility treatments and improving reproductive success rates.
Read on LinkedInDiagnosing human infertility involves a comprehensive evaluation of both partners to identify underlying causes and determine the most appropriate treatment approach. The diagnostic process begins with an initial consultation, including a detailed medical history, physical examination, and semen analysis. Female infertility evaluation includes ovulation testing, hormonal assessments, imaging studies, and, if necessary, laparoscopy to identify structural abnormalities. Male infertility evaluation involves semen analysis, hormonal testing, physical examination, and scrotal ultrasound to detect potential issues affecting sperm production and function. In cases of unexplained infertility, procedures such as intrauterine insemination (IUI) and in vitro fertilization (IVF) help assess fertilization success. Additional tests, including postcoital testing, antisperm antibody detection, and sperm DNA fragmentation analysis, provide further insights into reproductive health. A thorough diagnosis enables personalized treatment strategies, ranging from low-complexity interventions like ovulation induction and IUI to high-complexity assisted reproductive technologies (ART) such as IVF. Identifying the root causes of infertility ensures tailored and effective management, improving the chances of successful conception.
Read on LinkedInHuman infertility is a global health concern that affects millions of individuals and couples, impacting their emotional well-being, social relationships, economic stability, and overall reproductive health. The psychological effects of infertility include grief, anxiety, depression, and relationship strain, often leading to a loss of identity and self-worth. Social and cultural factors, such as stigma, community pressure, and traditional expectations surrounding childbearing, further exacerbate the challenges faced by individuals experiencing infertility. Economically, the high cost of fertility treatments, lost productivity, and expenses associated with adoption or surrogacy make family-building inaccessible for many. Additionally, infertility is closely linked to reproductive health issues, including sexually transmitted infections (STIs), polycystic ovary syndrome (PCOS), and endometriosis, which can further complicate conception. On a broader scale, infertility affects population dynamics, particularly in countries experiencing declining birth rates. Addressing infertility as a public health priority requires improving access to fertility treatments, comprehensive reproductive healthcare, and support systems that cater to diverse family structures. Recognizing the evolving nature of family composition, it is essential to provide inclusive reproductive healthcare solutions that respect and support the aspirations of all individuals and couples seeking to build families.
Read on LinkedInArtificial intelligence (AI) is revolutionizing the diagnosis of human infertility by improving accuracy, efficiency, and personalized care. AI-powered algorithms analyze extensive datasets, including medical histories, laboratory results, and imaging studies, to identify patterns and predict potential causes of infertility with greater precision. Key applications of AI in infertility diagnosis include predictive modeling, image analysis, natural language processing (NLP), and machine learning, all of which enhance decision-making and optimize fertility treatment strategies. AI-driven diagnostic tools such as semen analysis, ovulation prediction, and embryo selection are transforming assisted reproductive technologies (ART), improving the chances of successful conception. However, challenges such as data quality, regulatory approval, clinical validation, and ethical considerations must be addressed to ensure safe and effective AI deployment in reproductive healthcare. The future of AI in infertility diagnosis includes seamless integration with electronic health records (EHRs), personalized treatment planning, and expanded applications in predicting and preventing infertility-related complications. As AI continues to evolve, it holds the potential to enhance reproductive healthcare, making infertility diagnosis and treatment more accessible, precise, and patient-centered.
Read on LinkedInWe are excited to announce that EndoClassify, our cutting-edge AI-driven solution for endometrial receptivity analysis, is coming soon! This innovative technology is designed to seamlessly integrate into clinical workflows, empowering practitioners to make informed decisions while enhancing In Vitro Fertilization (IVF) success rates.Utilizing advanced artificial intelligence, including convolutional neural networks (Attention U-Net for segmentation and GoogleNet Inception for classification), EndoClassify achieves 95% accuracy in predicting endometrial receptivity. The technology seamlessly integrates into clinical workflows through two flexible deployment options: a secure API for direct system integration and a standalone mobile app for instant analysis via smartphone-captured ultrasound images. Beyond receptivity prediction, EndoClassify ensures data security by storing prediction results with traceability features, supporting continuous improvement in patient care. AI-driven innovations like EndoClassify address broader societal challenges, including declining birth rates and aging populations, by optimizing fertility treatments and advancing reproductive healthcare. As EndoClassify prepares for its official launch, clinics and healthcare providers worldwide can leverage this cutting-edge technology to enhance IVF success rates and transform patient outcomes.
Read on LinkedInThe First and Only AI Software for Measuring Endometrial Receptivity in IVF Success. The First and Only AI Software for Measuring Endometrial Receptivity in IVF Success /n We are thrilled to introduce Endoclassify, the first and only AI-powered software designed to measure endometrial receptivity in In Vitro Fertilization (IVF) processes. Based on the study “Artificial Intelligence Model Utilizing Endometrial Analysis to Contribute as a Predictor of Assisted Reproductive Technology Success”, Endoclassify offers unparalleled insights by utilizing advanced endometrial tissue analysis to predict the success of assisted reproductive technologies./nAs the only solution of its kind, Endoclassify revolutionizes fertility treatment by providing specialists with a powerful tool to precisely assess endometrial receptivity, a critical factor in IVF success. This innovation marks a new era in personalized, data-driven reproductive healthcare./nEndoclassify is a groundbreaking milestone in assisted reproduction, bringing cutting-edge technology to improve fertility outcomes for patients seeking to conceive through assisted reproductive techniques.
Read on LinkedIn
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Take a picture of the endometrial ultrasound
ensuring the entire ultrasound fits within the camera frame.
Verify the endometrial receptivity percentage and
determine whether it is good or bad
Our team, comprised of clinicians and engineers from Mexico and Argentina, collaborates with a unified goal: to unequivocally demonstrate the correlation between clinical research outcomes and artificial intelligence predictions in measuring endometrial receptivity.
This study addresses the development of EndoClassify, an artificial intelligence (AI) model designed to assess endometrial characteristics and enhance embryo receptivity. Utilizing a dataset of 402 endometrial ultrasound images augmented to 14.989, EndoClassify, incorporating Attention U-Net for image segmentation and GoogLeNet Inception for image classification, demonstrated exceptional performance with an accuracy of 95%, loss of 10%, a sensitivity of 93%, and specificity of 93%. The significance of EndoClassify extends beyond its robust metrics.
Ricardo H. Asch Schuff, Jorge Suarez, Nicolas Laugas, Marlene L. Zamora Ramirez, Tamar Alkon
In the context of In Vitro Fertilization (IVF), we introduce an innovative approach for assessing the endometrium via transvaginal ultrasound. Diverging from previous studies that centered on the comprehensive thickness and pattern of the endometrium, our methodology scrutinizes both the absolute and relative dimensions of its external layers, relative to the aforementioned factors.
Ricardo H. Asch Schuff, Tamar Alkon, Marlene L. Zamora Ramirez, Jorge Suarez, Nicolas Laugas
This study addresses the development of EndoClassify, an artificial intelligence (AI) model designed to assess endometrial characteristics and enhance embryo receptivity. Utilizing a dataset of 402 endometrial ultrasound images augmented to 14.989, EndoClassify, incorporating Attention U-Net for image segmentation and GoogLeNet Inception for image classification, demonstrated exceptional performance with an accuracy of 95%, loss of 10%, a sensitivity of 93%, and specificity of 93%. The significance of EndoClassify extends beyond its robust metrics.
Ricardo H. Asch Schuff, Jorge Suarez, Nicolas Laugas, Marlene L. Zamora Ramirez, Tamar Alkon
This study addresses the development of EndoClassify, an artificial intelligence (AI) model designed to assess endometrial characteristics and enhance embryo receptivity. Utilizing a dataset of 402 endometrial ultrasound images augmented to 14.989, EndoClassify, incorporating Attention U-Net for image segmentation and GoogLeNet Inception for image classification, demonstrated exceptional performance with an accuracy of 95%, loss of 10%, a sensitivity of 93%, and specificity of 93%. The significance of EndoClassify extends beyond its robust metrics.
Ricardo H. Asch Schuff, Jorge Suarez, Nicolas Laugas, Marlene L. Zamora Ramirez, Tamar Alkon
In the context of In Vitro Fertilization (IVF), we introduce an innovative approach for assessing the endometrium via transvaginal ultrasound. Diverging from previous studies that centered on the comprehensive thickness and pattern of the endometrium, our methodology scrutinizes both the absolute and relative dimensions of its external layers, relative to the aforementioned factors.
Ricardo H. Asch Schuff, Tamar Alkon, Marlene L. Zamora Ramirez, Jorge Suarez, Nicolas Laugas
This study addresses the development of EndoClassify, an artificial intelligence (AI) model designed to assess endometrial characteristics and enhance embryo receptivity. Utilizing a dataset of 402 endometrial ultrasound images augmented to 14.989, EndoClassify, incorporating Attention U-Net for image segmentation and GoogLeNet Inception for image classification, demonstrated exceptional performance with an accuracy of 95%, loss of 10%, a sensitivity of 93%, and specificity of 93%. The significance of EndoClassify extends beyond its robust metrics.
Ricardo H. Asch Schuff, Jorge Suarez, Nicolas Laugas, Marlene L. Zamora Ramirez, Tamar Alkon
This study addresses the development of EndoClassify, an artificial intelligence (AI) model designed to assess endometrial characteristics and enhance embryo receptivity. Utilizing a dataset of 402 endometrial ultrasound images augmented to 14.989, EndoClassify, incorporating Attention U-Net for image segmentation and GoogLeNet Inception for image classification, demonstrated exceptional performance with an accuracy of 95%, loss of 10%, a sensitivity of 93%, and specificity of 93%. The significance of EndoClassify extends beyond its robust metrics.
Ricardo H. Asch Schuff, Jorge Suarez, Nicolas Laugas, Marlene L. Zamora Ramirez, Tamar Alkon
1209 Mountain Road PL NE, STE R
Albuquerque, New Mexico
Zip Code 87110 USA
+15052954233
info@endoclassify.com
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