Objective: To develop a machine learning algorithm to detect rare human sperm in semen and microsurgical testicular sperm extraction (microTESE) samples using bright-field (BF) microscopy for nonobstructive azoospermia patients. Design: Spermatozoa were collected from fertile men. Testis biopsies were collected from microTESE samples determined to be clinically negative for sperm. A convolutional neural network based on the U-Net architecture was trained using 35,761 BF image patches with fluorescent ground truth image pairs to segment sperm. The algorithm was validated using 7,663 image patches. The algorithm was tested using 7,663 image patches containing abundant sperm, as well as 7,985 image patches containing rare sperm. Setting: In vitro fertilization center and university laboratories. Patient(s): Normospermic and nonobstructive azoospermia patients. Intervention(s): None. Main Outcome Measure(s): Precision (positive predictive value [PPV]), recall (sensitivity), and F1-score of detected sperm locations. Result(s): For sperm-only samples, our algorithm achieved 91% PPV, 95.8% sensitivity, and 93.3% F1-score at x10 magnification. For dissociated microTESE samples doped with an abundant quantity of sperm, our algorithm achieved 84.0% PPV, 72.7% sensitivity, and 77.9% F1-score. For dissociated microTESE samples doped with rare sperm, our algorithm achieved 84.4% PPV, 86.1% sensitivity, and 85.2% F1-score. Conclusion(s): Rare sperm can be detected in patients' testis biopsy samples for potential subsequent use in in vitro fertilization-intracytoplasmic sperm injection. A machine learning algorithm can use BF images at x10 magnification to accurately detect sperm locations using automated imaging. ((C) 2022 by American Society for Reproductive Medicine.)

Mobile robots are increasingly being deployed in public spaces such as shopping malls, airports, and urban sidewalks. Most of these robots are designed with human-aware motion planning capabilities but are not designed to communicate with pedestrians. Pedestrians encounter these robots without prior understanding of the robots’ behaviour, which can cause discomfort, confusion, and delayed social acceptance. In this research, we explore the common human-robot interaction at a doorway or bottleneck in a structured environment. We designed and evaluated communication cues used by a robot when yielding to a pedestrian in this scenario. We conducted an online user study with 102 participants using videos of a set of robot-to-human yielding cues. Results show that a Robot Retreating cue was the most socially acceptable cue. Repeated measures and Friedman’s ANOVAs on components of social acceptability were statistically significant (p = .01) and had small and medium effect sizes (η p 2 = .04, η p 2 = .08). The results of this work help guide the development of mobile robots for public spaces.

You have accessJournal of UrologyInfertility: Basic Research & Pathophysiology (PD39)1 Sep 2021PD39-12 DEEP LEARNING-BASED AUTOMATED SPERM IDENTIFICATION FOR NON-OBSTRUCTIVE AZOOSPERMIA PATIENTS Ryan Lee, Luke Witherspoon, Hongshen Ma, and Ryan Flannigan Ryan LeeRyan Lee More articles by this author , Luke WitherspoonLuke Witherspoon More articles by this author , Hongshen MaHongshen Ma More articles by this author , and Ryan FlanniganRyan Flannigan More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002049.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Over 30 million men worldwide are infertile, and approximately 15% of infertile men suffer from the most severe form of male infertility, non-obstructive azoospermia (NOA). NOA patients require andrologists to find viable sperm to proceed with in vitro fertilization (IVF) intracytoplasmic sperm injection (ICSI), which often requires hours seeking rare sperm under a microscope over tens of thousands of fields. We evaluate the feasibility of using machine learning methods for the identification of rare sperm in microscopy images taken from a semen sample to improve IVF success rates. METHODS: We prepared samples using density gradient centrifugation to isolate healthy sperm with no debris or non-sperm cells. Sperm are stained using SYBR-14 nucleic acid to be identified and then imaged using a wide-field fluorescent microscope on 96-well plates. After thresholding the fluorescent images using Otsu’s method, they are used as the ground truth and paired with bright field (BF) images to train a U-Net architecture using binary cross-entropy loss to segment sperm pixels. Individual sperm are identified using the watershed algorithm and evaluated through precision-recall metrics and receiver operating characteristic curves. RESULTS: Unlike previous work, the model is trained on BF images with unwashed and unstained samples to mimic clinical practice. A custom metric was developed in Python to evaluate the model on sperm prediction precision and recall using nearest-neighbor, a k-d tree, and size/distance thresholding. Pilot tests were completed to optimize model performance and speed to determine the use of 10x magnification. Heavily unbalanced datasets were counteracted using weighted losses. At 10x magnification, our model achieves 91% precision and 96% recall in finding sperm in microscopy BF semen images. CONCLUSIONS: Our results indicate it is feasible to use convolutional neural networks to semantically segment sperm to support andrologists for IVF-ICSI. Lowering the microscope magnification provides a greater rate of imaging by area with acceptance differences in sperm detection. Our custom lab protocol creates training data containing stained sperm and unstained miscellaneous cells, allowing for the first example of a real-world application of AI for assisted sperm identification. Source of Funding: New Frontiers Research Fund © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e672-e673 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ryan Lee More articles by this author Luke Witherspoon More articles by this author Hongshen Ma More articles by this author Ryan Flannigan More articles by this author Expand All Advertisement PDF downloadLoading ...

ABSTRACT Non-obstructive azoospermia (NOA), the most severe form of male infertility, is currently treated using microsurgical sperm extraction (microTESE) to retrieve sperm cells for in vitro fertilization via intracytoplasmic sperm injection (IVF-ICSI). The success rate of this procedure for NOA patients is currently limited by the ability of andrologists to identify a few rare sperm cells among millions of background testis cells. To improve this success rate, we developed a convolution neural network (CNN) to detect rare sperm from low-resolution microscopy images of microTESE samples. Our CNN uses the U-Net architecture to perform pixel-based classification on image patches from brightfield microscopy, which is followed by morphological analysis to detect individual sperm instances. This CNN is trained using microscopy images of fluorescently labeled sperm, which is fixed to eliminate their motility, and doped into testis biopsies obtained from NOA patients. We initially tested this algorithm using purified sperm samples at different imaging magnifications in order to determine the upper bounds of performance. We then tested this algorithm by doping rare sperm cells into testis biopsy samples from NOA patients and found a sperm detection F1 score of 85.2%. These results demonstrate the potential to use automated microscopy to dramatically increase the amount of testis biopsy tissue that could be comprehensively examined, which greatly increases the chance of finding rare viable sperm, and thereby increases the success rates of IVF-ICSI for couples with NOA.

Goal: COSMIC Medical, a Vancouver-based open-source volunteer initiative, has designed an accessible, affordable, and aerosol-confining non-invasive positive-pressure ventilator (NIPPV) device, known as the COSMIC Bubble Helmet (CBH). This device is intended for COVID-19 patients with mild-to-moderate acute respiratory distress syndrome. System Design: CBH is composed of thermoplastic polyurethane, which creates a flexible neck seal and transparent hood. This device can be connected to wall oxygen, NIPPVs including Continuous Positive Airway Pressure and Bi-level Positive Airway Pressure, and mechanical ventilators. Discussion: Justification of CBH design components relied on several factors, predominantly the safety and comfort of patients and healthcare providers. Conclusion: CBH has implications within and outside of the pandemic, as an alternative to invasive mechanical ventilation methods. We have experimentally verified that CBH is effective in minimizing aerosolization risks and performs at specified clinical requirements.