A non-invasive method for prediction of neurodegenerative diseases using gait signal features.

The steady degeneration of neurons is the hallmark of neurodegenerative illnesses, which are, by definition, incurable. Corticobasal Syndrome (CS), Huntington's Disease (HD), Dementia, Amyotrophic Lateral Sclerosis (ALS), Progressive supranuclear palsy (PSP) and Parkinson's Disease (PD) are some of the common neurodegenerative diseases which has impacted millions of people, predominantly among the older population. Various computational techniques, including but not limited to machine learning, are emerging as discrimination and detection of neuro-related diseases. This research proposed a machine learning-based framework to correctly detect PD, HD, and ALS from the gait signals of subjects both in binary and multi-class detection environment. The detection approach proposed here combines the classification power of Naïve Bayes and Logistic Regression jointly in a modern UltraBoost ensemble framework. The proposed method is unique in its ability to detect neuro diseases with a small number of gait features. The proposed approach ascertains most essential gait features through three state-of-the-art feature selection schemes, infinite feature selection, infinite latent feature selection and Sigmis feature selection. It has been observed that the gait signal features of the subjects are identified through Infinite Feature Selection manifests better detection results than the features obtained through Infinite Latent Feature and Sigmis feature selection while detecting Parkinson's and Huntington's Disease in a multi-class environment. So far as the binary detection environment is concern, the Amyotrophic lateral sclerosis is detected with 99.1% detection accuracy using 18 Sigmis gait features, with 99.1% sensitivity and 98.9% specificity, respectively. Similarly, Huntington's disease was detected with 94.2% detection accuracy, 94.2% sensitivity, and 94.5% specificity using 5 Sigmis gait features. Finally, Parkinson's disease was detected with 98.4% sensitivity, specificity, and detection accuracy.

An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images.

Recently, laryngeal cancer cases have increased drastically across the globe. Accurate treatment for laryngeal cancer is intricate, especially in the later stages. This type of cancer is an intricate malignancy inside the head and neck area of patients. In recent years, diverse diagnosis approaches and tools have been developed by researchers for helping clinical experts to identify laryngeal cancer effectively. However, these existing tools and approaches have diverse issues related to performance constraints such as lower accuracy in the identification of laryngeal cancer in the initial stage, more computational complexity, and large time consumption in patient screening. In this paper, the authors present a novel and enhanced deep-learning-based Mask R-CNN model for the identification of laryngeal cancer and its related symptoms by utilizing diverse image datasets and CT images in real time. Furthermore, our suggested model is capable of capturing and detecting minor malignancies of the larynx portion in a significant and faster manner in the real-time screening of patients, and it saves time for the clinicians, allowing for more patient screening every day. The outcome of the suggested model is enhanced and pragmatic and obtained an accuracy of 98.99%, precision of 98.99%, F1 score of 97.99%, and recall of 96.79% on the ImageNet dataset. Several studies have been performed in recent years on laryngeal cancer detection by using diverse approaches from researchers. For the future, there are vigorous opportunities for further research to investigate new approaches for laryngeal cancer detection by utilizing diverse and large dataset images.

Prophylaxis for preventing venous thromboembolism in knee arthroscopy and soft tissue reconstruction: consensus statements from an international panel of experts.

PURPOSE: There is a lack of consensus regarding need for Venous Thrombo Embolism (VTE) prophylaxis following arthroscopic knee surgery and open soft tissue knee reconstruction. Clear cut guidelines like ones for trauma surgery and arthroplasty do not exist and the published literature is limited to case reports with a few society guidelines. Given this lack of consensus, we conducted a modified Delphi questionnaire of international experts to provide recommendations on this topic. METHODS: The consensus statements were generated using an anonymised 3 round modified Delphi questionnaire, sent to an international panel of 38 knee surgeons, with an 80% agreement being set as the limit for consensus. The responses were analysed using descriptive statistics with measures like mode, median and box plots. Feedback was provided to all panelists based on responses from the previous rounds to help generate the consensus. RESULTS: Six consensus statements were generated after the three rounds of Delphi. Patient factors, prolonged surgery duration and family history of thrombogenic events emerged as the main points to be taken into consideration for prophylaxis. CONCLUSION: It was established through this study, that there exists a select group of patients undergoing arthroscopic surgery that justify the usage of VTE prophylaxis. The expert responses to most of the questions in different scenarios favoured usage of VTE prophylaxis based on patient factors like advanced age, past history of VTE, smoking, oral contraceptive use etc. LEVEL OF EVIDENCE: Level V.