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Central disorders of hypersomnolence (CDH) are chronic diseases that significantly impact the lives of affected individuals. We aimed to explore the perspectives of individuals with narcolepsy type 1 (NT1), narcolepsy type 2 (NT2), and idiopathic hypersomnia (IH), and the challenges they encounter in their daily lives and within the healthcare systems in the Nordics. Interviews with patients (N = 41) and healthcare professionals (n = 14) and a patient survey (n = 70) were conducted in 2022 in Denmark, Sweden, Finland, and Norway to develop a patient journey map that visualises the patient with CDH journey and provides insights into the difficulties faced by these individuals. The patient journey mapping approach was chosen to focus on the processes and experiences of patients, highlighting the challenges they confront. Our findings revealed that the process of receiving a CDH diagnosis, as well as subsequent misdiagnoses and treatment, can be protracted and burdensome. CDH diagnoses remain poorly understood by neurologists, general practitioners, and the public, resulting in adverse consequences, with patients reporting a mean (standard deviation [SD]) time from symptom onset to diagnosis of 8.4 (5.11) years and a mean (SD) of 5.5 (4.17) productive hours lost/day. The available non-pharmaceutical support for patients with CDH, encompassing medical, psychological, educational, and professional assistance, was insufficient. The generalisability of the findings to one specific diagnosis is limited due to the collective analysis of the CDH. These findings are invaluable for identifying disruptions in the patient with CDH journeys and for designing improved pathways for those with NT1, NT2, and IH in the future., (© 2024 Takeda Pharmaceuticals International AG. Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dataset including 181,428 embryos from 22 IVF clinics across the world. To discriminate the transferred embryos with known outcome, we show areas under the receiver operating curve ranging from 0.621 to 0.707 depending on the day of transfer. Predictive performance increased over time and showed a strong correlation with morphokinetic parameters. The model's performance is equivalent to the KIDScore D3 model on day 3 embryos while it significantly surpasses the performance of KIDScore D5 v3 on day 5+ embryos. This model provides an analysis of time-lapse sequences without the need for user input, and provides a reliable method for ranking embryos for their likelihood of implantation, at both cleavage and blastocyst stages. This greatly improves embryo grading consistency and saves time compared to traditional embryo evaluation methods., (© 2023. The Author(s).)

Assessing and selecting the most viable embryos for transfer is an essential part of in vitro fertilization (IVF). In recent years, several approaches have been made to improve and automate the procedure using artificial intelligence (AI) and deep learning. Based on images of embryos with known implantation data (KID), AI models have been trained to automatically score embryos related to their chance of achieving a successful implantation. However, as of now, only limited research has been conducted to evaluate how embryo selection models generalize to new clinics and how they perform in subgroup analyses across various conditions. In this paper, we investigate how a deep learning-based embryo selection model using only time-lapse image sequences performs across different patient ages and clinical conditions, and how it correlates with traditional morphokinetic parameters. The model was trained and evaluated based on a large dataset from 18 IVF centers consisting of 115,832 embryos, of which 14,644 embryos were transferred KID embryos. In an independent test set, the AI model sorted KID embryos with an area under the curve (AUC) of a receiver operating characteristic curve of 0.67 and all embryos with an AUC of 0.95. A clinic hold-out test showed that the model generalized to new clinics with an AUC range of 0.60-0.75 for KID embryos. Across different subgroups of age, insemination method, incubation time, and transfer protocol, the AUC ranged between 0.63 and 0.69. Furthermore, model predictions correlated positively with blastocyst grading and negatively with direct cleavages. The fully automated iDAScore v1.0 model was shown to perform at least as good as a state-of-the-art manual embryo selection model. Moreover, full automatization of embryo scoring implies fewer manual evaluations and eliminates biases due to inter- and intraobserver variation., Competing Interests: This study was supported by Vitrolife, the employer of J.B., J.R., J.T.L, M.F.K. All authors participated in the study design, data collection and analysis and preparation of the manuscript. The decision to publish was taken by J.B. Vitrolife produces and markets iDAScore. The study was also supported by Harrison.AI, the employer of D.T. He participated in the study design, data collection and analysis. D.T. has a patent related the current study. J.B. and J.R. are Vitrolife A/B shareholders. This does not alter our adherence to PLOS ONE policies on sharing data and materials, as detailed online in our guide for authors.

With self-supervised learning, both labeled and unlabeled data can be used for representation learning and model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during a fertility treatment, in which only the embryos that were transferred to the female uterus may have labels of pregnancy. In this paper, we apply a self-supervised video alignment method known as temporal cycle-consistency (TCC) on 38176 time-lapse videos of developing embryos, of which 14550 were labeled. We show how TCC can be used to extract temporal similarities between embryo videos and use these for predicting pregnancy likelihood. Our temporal similarity method outperforms the time alignment measurement (TAM) with an area under the receiver operating characteristic (AUC) of 0.64 vs. 0.56. Compared to existing embryo evaluation models, it places in between a pure temporal and a spatio-temporal model that both require manual annotations. Furthermore, we use TCC for transfer learning in a semi-supervised fashion and show significant performance improvements compared to standard supervised learning, when only a small subset of the dataset is labeled. Specifically, two variants of transfer learning both achieve an AUC of 0.66 compared to 0.63 for supervised learning when 16% of the dataset is labeled.

Background: Blastocyst morphology is a predictive marker for implantation success of in vitro fertilized human embryos. Morphology grading is therefore commonly used to select the embryo with the highest implantation potential. One of the challenges, however, is that morphology grading can be highly subjective when performed manually by embryologists. Grading systems generally discretize a continuous scale of low to high score, resulting in floating and unclear boundaries between grading categories. Manual annotations therefore suffer from large inter-and intra-observer variances., Method: In this paper, we propose a method based on deep learning to automatically grade the morphological appearance of human blastocysts from time-lapse imaging. A convolutional neural network is trained to jointly predict inner cell mass (ICM) and trophectoderm (TE) grades from a single image frame, and a recurrent neural network is applied on top to incorporate temporal information of the expanding blastocysts from multiple frames., Results: Results showed that the method achieved above human-level accuracies when evaluated on majority votes from an independent test set labeled by multiple embryologists. Furthermore, when evaluating implantation rates for embryos grouped by morphology grades, human embryologists and our method had a similar correlation between predicted embryo quality and pregnancy outcome., Conclusions: The proposed method has shown improved performance of predicting ICM and TE grades on human blastocysts when utilizing temporal information available with time-lapse imaging. The algorithm is considered at least on par with human embryologists on quality estimation, as it performed better than the average human embryologist at ICM and TE prediction and provided a slightly better correlation between predicted embryo quality and implantability than human embryologists., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Multiple neurological disorders including Alzheimer's disease (AD), mesial temporal sclerosis, and mild traumatic brain injury manifest with volume loss on brain MRI. Subtle volume loss is particularly seen early in AD. While prior research has demonstrated the value of this additional information from quantitative neuroimaging, very few applications have been approved for clinical use. Here we describe a US FDA cleared software program, NeuroreaderTM, for assessment of clinical hippocampal volume on brain MRI., Objective: To present the validation of hippocampal volumetrics on a clinical software program., Method: Subjects were drawn (n = 99) from the Alzheimer Disease Neuroimaging Initiative study. Volumetric brain MR imaging was acquired in both 1.5 T (n = 59) and 3.0 T (n = 40) scanners in participants with manual hippocampal segmentation. Fully automated hippocampal segmentation and measurement was done using a multiple atlas approach. The Dice Similarity Coefficient (DSC) measured the level of spatial overlap between NeuroreaderTM and gold standard manual segmentation from 0 to 1 with 0 denoting no overlap and 1 representing complete agreement. DSC comparisons between 1.5 T and 3.0 T scanners were done using standard independent samples T-tests., Results: In the bilateral hippocampus, mean DSC was 0.87 with a range of 0.78-0.91 (right hippocampus) and 0.76-0.91 (left hippocampus). Automated segmentation agreement with manual segmentation was essentially equivalent at 1.5 T (DSC = 0.879) versus 3.0 T (DSC = 0.872)., Conclusion: This work provides a description and validation of a software program that can be applied in measuring hippocampal volume, a biomarker that is frequently abnormal in AD and other neurological disorders.