Teacher in Space Project

No abstract availabl

Supervised machine learning in psychiatry:towards application in clinical practice

In recent years, the field of machine learning (often named with the more general term artificial intelligence) has literally exploded and its application has been proposed in basically all fields, including psychiatry and mental health. This has been motivated by the promise of using machine learning to develop new clinical tools that could help perform personalized predictions and recommendations, ultimately improving the results achievable in the psychiatric clinical practice that still faces only a limited success in the fight against mental diseases. However, despite this huge interest, there is still a substantial lack of tools in psychiatry that are based on machine learning algorithms. Massimiliano Grassi, in his Ph.D. thesis, investigates the challenges of translating machine learning algorithms into clinical practice and proposes innovative solutions to these challenges. The thesis presents the development and validation of new algorithms for the prediction of the onset of Alzheimer’s disease, the remission of obsessive-compulsive disorder, and the automatization of sleep staging in polysomnography, a method to diagnose sleep disorders. The results from these studies demonstrate that the use of machine learning in psychiatric clinical practice is not just a promise, and it is possible to develop machine learning algorithms that achieve clinically relevant performance even if based solely on information that can be easily accessible in the daily clinical routine

Moving from Novice to Expertise and Its Implications for Instruction

Objective: To address the stages of expertise development, what differentiates a novice from an expert, and how the development and differences impact how we teach our classes or design the curriculum. This paper will also address the downside of expertise and discuss the importance of teaching expertise relative to domain expertise

Source-free Domain Adaptation for Sleep Stage Classification

The popularity of machine learning algorithms has increased in recent years as data volumes have risen, algorithms have advanced, and computational power and storage have improved. EEG-based sleep staging has become one of the most active research areas over the last decade. Labeling each sleep stage manually is a labor-intensive and time-consuming process that requires expertise, making it susceptible to human error. In the meantime, training models on an unseen dataset remains challenging due to physiological differences between subjects and electrode sensor configurations. Unsupervised domain adaptation approaches may provide a solution to this problem by borrowing knowledge from a labeled dataset to train an unlabeled dataset. A source-free unsupervised domain adaptation methodology is employed in this thesis to solve the problem of automatic single-channel EEG sleep stage classification. Our study shows that pre-training source domain models followed by supervised fine-tuning improve the learned representations when applied to EEG sleep signals. We further develop weighted diversity loss in order to achieve a model that outperforms state-of-the-art unsupervised domain adaptation techniques without access to source domain data

25th International Congress of the European Association for Endoscopic Surgery (EAES) Frankfurt, Germany, 14-17 June 2017 : Oral Presentations

Introduction: Ouyang has recently proposed hiatal surface area (HSA) calculation by multiplanar multislice computer tomography (MDCT) scan as a useful tool for planning treatment of hiatus defects with hiatal hernia (HH), with or without gastroesophageal reflux (MRGE). Preoperative upper endoscopy or barium swallow cannot predict the HSA and pillars conditions. Aim to asses the efficacy of MDCT’s calculation of HSA for planning the best approach for the hiatal defects treatment. Methods: We retrospectively analyzed 25 patients, candidates to laparoscopic antireflux surgery as primary surgery or hiatus repair concomitant with or after bariatric surgery. Patients were analyzed preoperatively and after one-year follow-up by MDCT scan measurement of esophageal hiatus surface. Five normal patients were enrolled as control group. The HSA’s intraoperative calculation was performed after complete dissection of the area considered a triangle. Postoperative CT-scan was done after 12 months or any time reflux symptoms appeared. Results: (1) Mean HSA in control patients with no HH, no MRGE was cm2 and similar in non-complicated patients with previous LSG and cruroplasty. (2) Mean HSA in patients candidates to cruroplasty was 7.40 cm2. (3) Mean HSA in patients candidates to redo cruroplasty for recurrence was 10.11 cm2. Discussion. MDCT scan offer the possibility to obtain an objective measurement of the HSA and the correlation with endoscopic findings and symptoms. The preoperative information allow to discuss with patients the proper technique when a HSA[5 cm2 is detected. During the follow-up a correlation between symptoms and failure of cruroplasty can be assessed. Conclusions: MDCT scan seems to be an effective non-invasive method to plan hiatal defect treatment and to check during the follow-up the potential recurrence. Future research should correlate in larger series imaging data with intraoperative findings

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.Comment: Under Revie

Towards Automating Sleep Stage Scoring to Diagnose Sleep Disorders

Overnight polysomnography (PSG) is an important tool used to characterize sleep and the gold standard procedure for diagnosing many sleep disorders. PSG is a non-invasive procedure that collects various physiological data, such as EEG, EMG, EOG and ECG signals. The data is then scored in a subjective, laborious and time-consuming process by sleep specialists who assign a sleep stage to every 30-second window of the data according to predefined scoring rules by the American Academy of Sleep Medicine (AASM). Finally, clinicians make a diagnosis based on this annotated data. Consequently, the current process is heavily dependent upon human factors, which can result in poor agreement between expert scorers, but inter-scorer reliability has been found to be only around 82%. In this study we developed an automatic sleep stage scoring method, using a likelihood ratio decision tree classifier, with the goal of improving the speed, reliability, accuracy and cost efficiency of the current PSG scoring process. The algorithm was developed using the AASM Manual for Scoring Sleep. We extracted features from various physiological recordings of the PSG, based on the predefined rules of the AASM Manual. The features were computed for each 30-second epoch, in either the time or the frequency domain. The most useful features were selected by looking at probability distributions for each metric conditioned on the sleep stage, and identifying the features giving the greatest separation between stages. Examples of meaningful features include the power in different frequency bands of EEG signals, EMG energy per epoch, and number of spindles per epoch, to mention a few. These features were then used as inputs to the classifier which assigned each epoch one of five possible stages:; N3, N2, N1, REM or Wake. The automatic scoring was trained and tested on PSG data from 39 healthy individuals (age range: 24.2±3.1 years) with no sleep disturbances. The overall scoring accuracy was 76.97% on the test set. Some of the stages, such as stage N2, have more distinctive characteristics and thus yielded a higher per-stage scoring accuracy, whereas the other stages, for example stages N1 and REM, got confused more easily, resulting in lower per-stage accuracies. As expected, most misclassifications occurred between adjacent sleep stages. Although this accuracy may at first seem low, it is likely that the stages that the tool classified inaccurately may be sleep stages that contribute to inter-scorer reliability. Therefore, we see this tool as assisting sleep scorers to enhance efficiency with the further goal of eventually improving inter-scorer reliability. Sleep stage scoring provides an important basis for diagnosis of sleep disorders in general. However, the detection of sleep disturbances is very costly and time-consuming, and relies on subjective measures. Automating the scoring process improves the efficiency and consistency of scoring procedures and offers a way to diagnose sleeping disorders in a more robust, quantitative manner

Ariel - Volume 5 Number 1

Editors Mark Dembert J.D. Kanofskv Entertainment Editor Robert Breckenridge Gary Kaskey Editor Emeritus David A. Jacoby Photographer Scott Kastner Staff Richard Blutstein Bob Johnson John R. Cohn Joseph Sassani Ken Jaffe Bob Sklarof