Title: Advancing Sperm Analysis with AI: Innovations in Motility Assessment and Morphological Classification

Abstract:

This talk will present ongoing efforts to enhance sperm analysis using artificial intelligence, focusing on improving precision and consistency in motility and morphological assessments—key metrics in reproductive health studies. We will discuss comparative analyses of sperm motility measured through a computer-assisted sperm analysis (CASA) system, manual tracking methods, and a calibrated AI-driven model, highlighting findings on measurement variability and potential biases, alongside the promise and limitations of AI for consistent tracking. Additionally, we will explore a machine learning approach for sperm morphology classification using ensemble models that combine various convolutional neural network architectures.

Bio:

Klaus Ackermann is a Senior Lecturer  in the Department of Econometrics and Business Statistics at Monash University in Melbourne, Australia. His research now primarily focuses on developing econometric methods for causal inference and project evaluation, particularly in contexts where spillovers between treatment and control groups are likely. A secondary area is to use of neural networks for image processing in remote sensing and medical applications. He holds a PhD in Economics from Monash University and a BSc and an MSc in Business Informatics, with a major in
Economics, from the Technical University of Vienna. Following his academic training, he served as a postdoctoral fellow at the Center for Data Science
and Public Policy at the University of Chicago.