#Scientists have developed artificial ultra-soft, gel like... #platelets that can help in #clotting @the_hindu #thalassemia #blooddisorder #geneticdisorder #geneticdisease #sicklecell #MyHealth

Our research "Efcient white #blood #cell #identifcation with hybrid #inception?xception #network" has just been published by The Journal of Supercomputing. A big thank you brother RADHWAN ALI ABDULGHANI SALEH, Mustafa Ghaleb and respected H. Metin Ertunc. This research study recommends an ingenious #hybrid #inception-#xception #Convolutional #Semantic #network (CNN) designed to deal with constraints in existing #Deep #Learning versions. The proposed #network #incorporates inception and #depth-separable #convolution layers to successfully catch attributes across many ranges, as detailed in the article, accessible at: https://lnkd.in/gNGk9S-D #WhiteBloodCellIdentification #HybridInceptionXception #EfficientCellDetection #MedicalImaging #DeepLearning #MachineLearning #AIinHealthcare #BiomedicalEngineering #ImageProcessing #HealthcareInnovation #NeuralNetworks #MedicalResearch #AIApplications #CellDetection #DigitalPathology #MedicalAI #DataScience #HealthcareTech #PrecisionMedicine #Biotech

An inherent limitation of machine learning models is the retrospective analysis of data and the complex interpretation of results. Therefore, in collaboration with the team at Delta Fertility Clinic, we have embarked on a prospective study to assess the applicability of our neural network model in embryo transfers following PGT. Conducting this study in Bishkek will allow us to validate our neural network for the patient population in Asia and compare the results not only with retrospective data but also with other patient groups from Europe. Additionally, within the clinic, we aim to develop a unique quality control and risk prevention system based on artificial intelligence algorithms, identifying threshold values for concern and enabling rapid response to changes in the effectiveness of our work. #MachineLearningModels #ProspectiveStudy #NeuralNetworkApplication #ArtificialIntelligenceInHealthcare #QualityControl #IVFRiskPrevention #HealthcareInnovation #FertilityResearch #BishkekIVF #IVFAsiaPopulation #EuropeanComparison #ClinicalDataAnalysis

Developments in the AI and machine learning space of reproductive medicine continue at pace. This new paper from Akhil Garg, Jose Bellver, Ernesto Bosch, José Alejandro Remohí, Antonio Pellicer, and Marcos Meseguer Escrivá states that the introduction of AI-based algorithms to the ovarian stimulation (OS) cycle would help physicians in clinical decision making and provide support to patients with early insights to make helpful decisions. They present an early insight tool for predicting the number of oocytes and trigger day in the planning of OS cycles, showing its predictive power in clinical settings. This study highlights that ovarian response is not only limited to ovarian reserve or age but also to other parameters, which were not considered in conventional evaluation and studies. The model predicts high accuracy in clinical settings and is a promising tool for personalized treatment of controlled OS in a non-biased manner. Though promising for practical settings, the authors do note that these models need to be validated in global clinical settings for generalizability in future research, with a focus on customization for different use cases, as well as improvement of outcomes by introducing more information and data. The full paper is available now as a pre-proof to RBMO subscribers from our in-press articles section now: https://lnkd.in/ecpRg-Tb #ClinicalAssistedReproductiveTechnology #ArtificialIntelligence #OvarianStimulation #ArtificialNeuralNetwork #MachineLearning #MiiOutcomes

Here's a 1 min pitch recorded at a Mediterranean seaside bar ??? explaining multi-sensors we develop at InSpek, with a little help of ???? Technically a re-post of a health.tech conference submission, but un-cropped version of it with a few more details. Let me know if you want to know more about the smallest and the most comprehensive multi-sensors for #bioprocesses! #photonicintegratedcircuits #raman #ramansensing #ramanonchip #multisensor #bioprocessing #biopharma #syntheticbiology #Healthcare #sustainability #AI #sensing #optics #

Surely this is was not among the top ?user experiences‘ how marketing would probably want to label it. But highly recommended when you approach or let behind your dimidium saeculum (50ys) and especially when you’ve had incidents of cancer in the digestive system within your closer family. The medical check is not so bad after all, and you owe it to yourself and your beloved. So, better not procrastinate and do your check ups for early detection and effective countermeasures! It is comforting to know there is powerfully effective #AI used for image interpretation and pre-diagnostics to assist your knowledgeable medical experts. #gastroenterology #computervision #ai #clinicalapplications #deeplearning #diagnostics #screening #checkup #cancerprevention

Yesterday my son Joakim Ronstroem reached a milestone in his career in defending his Ph.D thesis. Being a father and seeing your kids grow, make mistakes and successes is an interesting thing, you learn a lot of unexpected things. So when you hold your baby in your arms for the first time you have no idea of what will happen to your kid and you will be in for a number of surprises :) His area of research is very different from mine. He specialises in Neuroscience, that is trying to understand how the brain works and what makes it tick. My area of research is to build machines that mimics how the brain works one could say. To understand my sons research paper I found that ChatGPT is a nice tool. Helps explaining all the special words used in this area. Here is one of the papers from his Ph.D thesis: https://lnkd.in/die8UAGK

LATEST IN HEALTH TECH : Quick Read ! Ai Model to Detect Male Infertility ! A study by Toho University, led by Associate Professor Hideyuki Kobayashi, developed an AI model that predicts male infertility risk using blood hormone levels, bypassing the need for semen analysis. The AI, created with no-programming-required software, was trained on data from 3,662 patients and achieved an overall accuracy of approximately 74%, with 100% accuracy in predicting non-obstructive azoospermia, the most severe form of male infertility. Hormone levels of LH, FSH, PRL, testosterone, and E2 were measured. Validation using 2021 and 2022 data showed accuracy rates of 58% and 68%, respectively. This model serves as a primary screening tool, aiding in early detection and subsequent detailed testing. The study was published in 'Scientific Reports' on July 31, 2024. #maleinfertility #aidetection Scientific Reports

???? A Comprehensive Examination of ChatGPT's Contribution to the Healthcare Sector and Hepatology ?? Kabita Kumari ???? https://lnkd.in/e7K49hQj ?? Focus on data insights: - ?? Artificial Intelligence

We believe that the current healthcare model is going to bring miracles to help you get rid of chronic diseases. The advent of new age healthtech startups, so called prefix “ Artificial Intelligence” & wearable tech has further reinforced this Unfortunately, this is not going to happen!We are daydreaming!? Let us deep drive We have been spending a lot on drugs( > Rs.6K-10k a month), CGM/Smart rings(Rs14k to 18K a month), OTC supplements(> Rs.6k a month) & obviously health insurance but the more we spend more our health is deteriorating Have you ever wondered why? All the existing interventions as mentioned above are focussed on managing the post disease symptoms you already have & making you subscribe for synthetic compounds to suppress specific biochemical functions. Obviously, these interventions work in isolation so managing a couple of symptoms are going to make you develop a few more & what happens next is a vicious cycle. Why is this happening? We have all tech & AI in place? Tech, AI, ML or whatever these big words companies use- these are systems, processors. These systems have to be fed with the right inputs to get the insights/outputs that could drive health outcomes. But the current healthcare model is feeding these systems with wrong data points- Healthcare delivery data- “ the summary/post mortem of biological processes that cannot deep dive into changes in biochemistry before the disease manifests. We need to focus on molecular data that identifies epigenetic changes- what genes are being expressed, what molecules are being secreted, what biochemical functions are taking place at molecular level & so on. This is what we at Genefitletics have been doing to deeply understand the mechanism of disease onset & progression. We are creating a parallel healthcare system by utilising our fully functional system biology platform- PROTEBA which collects, analyses & mines molecular, clinical & longitudinal metadata to discover asymptomatic/hard to detect signals underlying range of chronic diseases & cancer & provide disease modifying interventions. Our machine learnt models can identify & measure pre-disease signals for type 2 diabetes, CVD & more, representing ~ 50% of chronic diseases & cancer. Today we are sharing insights from the molecular data we have mined till now representing biochemical functions of gut & oral microbiome. Worth watching this short video : https://lnkd.in/gsPA32pJ #oralhealth #gutmicrobiome #chronicdiseases #cancer #artificialintelligence #machinelearning #oralcancer #oncology #type2diabetes #cardiovasculardiseases Sakshi Bali Shashi Shekhar M. Dr Kamal Karnatak Rupinder Singh Tarun Kumar