Comprehensive supervised heavy training program versus home training regimen in patients with subacromial impingement syndrome: a randomized trial

Background There is no consensus on the best training regimen for subacromial impingement syndrome (SIS). Several have been suggested, but never tested. The purpose of the study is to compare a comprehensive supervised training regimen (STR) based on latest evidence including heavy slow resistance training with a validated home-based regimen (HTR). We hypothesized that the STR would be superior to the HTR. Methods Randomised control trial with blinded assessor. 126 consecutive patients with SIS were recruited and equally randomised to 12 weeks of either supervised training regimen (STR), or home-based training regimen (HTR). Primary outcomes were Constant Score (CS) and Shoulder Rating Questionnaire (SRQ) from baseline and 6 months after completed training. Results were analyzed according to intention-to treat principles. The study was retrospectively registered in ClinicalTrials.gov. Date of registration: 07/06/2021. Identification number: NCT04915430. Results CS improved by 22.7 points for the STR group and by 23,7 points for the HTR (p = 0.0001). The SRQ improved by 17.7 and 18.1 points for the STR and the HTR groups respectively (p = 0.0001). The inter-group changes were non-significant. All secondary outcomes (passive and active range of motion, pain on impingement test, and resisted muscle tests) improved in both groups, without significant inter-group difference. Conclusion We found no significant difference between a comprehensive supervised training regimen including heavy training principles, and a home-based training program in patients with SIS.

Visual outcomes and intraoperative complications of small incision cataract surgery performed by junior residents in GMC

To evaluate in intraoperative complications of MSICS performed by Junior Residents and to compare the incidences of major complications in the first six months of training versus last six months of training.It is a retrospective type of study. From March 2018 to February 2019 a total of 293 manual SICS were conducted by the Junior Residents in Department of Ophthalmology. Each of the patients underwent a detailed ophthalmological examination preoperatively and underwent MSICS under peribulbar anesthesia. Consents for surgeries were obtained from each patient.Following intraoperative complications were noted: tunnel related complications, capsulorrhexis related complications, Iridodialysis, posterior capsular rent, zonular dialysis, vitreous leak, surgical aphakia, Descemet membrane detachment, placement of ACIOL, Nucleus drop and IOL drop. The patient’s immediate postoperative vision was also noted. SPSS version 15.0.Tunnel related complications were found in 13.98% patients either as scleral button holing or premature entry. Posterior capsular rents and bag disinsertion were found in total of 11.94% patients owing to which 3.07% were left aphakic. 63.13% patients had visual acuity better than 6/12 by snellens chart on first postoperative day. Performance of adequate anterior capsulotomy, minimal handling of the cornea and avoidance of posterior capsular rent are some of the challenges faced by the residents while learning MSICS. Stepwise supervised training can help a resident doctor master these steps while keeping the complications at acceptably low levels. Stepwise supervised training of residents performing MSICS can minimize complications

Automatic Processing Pipeline for Collecting and Annotating Air-Traffic Voice Communication Data

This document describes our pipeline for automatic processing of ATCO pilot audio communication we developed as part of the ATCO2 project. So far, we collected two thousand hours of audio recordings that we either preprocessed for the transcribers or used for semi-supervised training. Both methods of using the collected data can further improve our pipeline by retraining our models. The proposed automatic processing pipeline is a cascade of many standalone components: (a) segmentation, (b) volume control, (c) signal-to-noise ratio filtering, (d) diarization, (e) ‘speech-to-text’ (ASR) module, (f) English language detection, (g) call-sign code recognition, (h) ATCO—pilot classification and (i) highlighting commands and values. The key component of the pipeline is a speech-to-text transcription system that has to be trained with real-world ATC data; otherwise, the performance is poor. In order to further improve speech-to-text performance, we apply both semi-supervised training with our recordings and the contextual adaptation that uses a list of plausible callsigns from surveillance data as auxiliary information. Downstream NLP/NLU tasks are important from an application point of view. These application tasks need accurate models operating on top of the real speech-to-text output; thus, there is a need for more data too. Creating ATC data is the main aspiration of the ATCO2 project. At the end of the project, the data will be packaged and distributed by ELDA.

Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data

The new coronavirus has caused more than one million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography ( CT ) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. Our motivation is to develop an automatic method that can cope with scenarios in which preparing labeled data is time consuming or expensive. In this article, we propose a Semi-supervised Classification using Limited Labeled Data ( SCLLD ) relying on Sobel edge detection and Generative Adversarial Networks ( GANs ) to automate the COVID-19 diagnosis. The GAN discriminator output is a probabilistic value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid Hospital, whereas a public dataset is also used for validating our system. The proposed method is compared with other state-of-the-art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a semi-supervised method for COVID-19 detection is presented. Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data. Thus, our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network ( CNN ) when labeled training data is scarce. The 95% confidence intervals for our method in terms of accuracy, sensitivity, and specificity are 99.56 ± 0.20%, 99.88 ± 0.24%, and 99.40 ± 0.18%, respectively, whereas intervals for the CNN (trained supervised) are 68.34 ± 4.11%, 91.2 ± 6.15%, and 46.40 ± 5.21%.

SSL-Unet: A Self-Supervised Learning Strategy Base on U-Net for Retinal Vessel Segmentation

We propose a SSL-Unet model for retinal vascular segmentation as well as two self-supervised training strategies. The strategy can help the self-supervised module to learn pseudo labels for improving the segmentation performance. Moreover, the fusion of both self-supervised and supervised paradigms is applied to retinal segmentation for the first time. Meanwhile, it can also be extended to any segmentation network.

SSL-Unet: A Self-Supervised Learning Strategy Base on U-Net for Retinal Vessel Segmentation

We propose a SSL-Unet model for retinal vascular segmentation as well as two self-supervised training strategies. The strategy can help the self-supervised module to learn pseudo labels for improving the segmentation performance. Moreover, the fusion of both self-supervised and supervised paradigms is applied to retinal segmentation for the first time. Meanwhile, it can also be extended to any segmentation network.

Unsupervised Learning with Generative Adversarial Network for Automatic Tire Defect Detection from X-ray Images

Automatic defect detection of tire has become an essential issue in the tire industry. However, it is challenging to inspect the inner structure of tire by surface detection. Therefore, an X-ray image sensor is used for tire defect inspection. At present, detection of defective tires is inefficient because tire factories commonly conduct detection by manually checking X-ray images. With the development of deep learning, supervised learning has been introduced to replace human resources. However, in actual industrial scenes, defective samples are rare in comparison to defect-free samples. The quantity of defective samples is insufficient for supervised models to extract features and identify nonconforming products from qualified ones. To address these problems, we propose an unsupervised approach, using no labeled defect samples for training. Moreover, we introduce an augmented reconstruction method and a self-supervised training strategy. The approach is based on the idea of reconstruction. In the training phase, only defect-free samples are used for training the model and updating memory items in the memory module, so the reproduced images in the test phase are bound to resemble defect-free images. The reconstruction residual is utilized to detect defects. The introduction of self-supervised training strategy further strengthens the reconstruction residual to improve detection performance. The proposed method is experimentally proved to be effective. The Area Under Curve (AUC) on a tire X-ray dataset reaches 0.873, so the proposed method is promising for application.

Feasibility and Efficiency of the BEFORE (Better Exercise and Food, Better Recovery) Prehabilitation Program

Prehabilitation has been postulated as an effective preventive intervention to reduce postoperative complications, particularly for elderly patients with a relatively high risk of complications. To date, it remains to be determined whether prehabilitation increases physical capacity and reduces postoperative complications. The aim of this study was to assess the feasibility of a 4-week multimodal prehabilitation program consisting of a personalized, supervised training program and nutritional intervention with daily fresh protein-rich food for colorectal cancer patients aged over 64 years prior to surgery. The primary outcome was the feasibility of this prehabilitation program defined as ≥80% compliance with the exercise training program and nutritional intervention. The secondary outcomes were the organizational feasibility and acceptability of the prehabilitation program. A compliance rate of ≥80% to both the exercise and nutritional intervention was accomplished by 6 patients (66.7%). Attendance of ≥80% at all 12 training sessions was achieved by 7 patients (77.8%); all patients (100%) attended ≥80% of the available training sessions. Overall, compliance with the training was 91.7%. Six patients (66.7%) accomplished compliance of ≥80% with the nutritional program. The median protein intake was 1.2 (g/kg/d). No adverse events occurred. This multimodal prehabilitation program was feasible for the majority of patients.