A comprehensive bibliometric survey of micro-expression recognition system based on deep learning.

Micro-expressions (ME) are rapidly occurring expressions that reveal the true emotions that a human being is trying to hide, cover, or suppress. These expressions, which reveal a person's actual feelings, have a broad spectrum of applications in public safety and clinical diagnosis. This study provides a comprehensive review of the area of ME recognition. A bibliometric and network analysis techniques is used to compile all the available literature related to ME recognition. A total of 735 publications from the Web of Science (WOS) and Scopus databases were evaluated from December 2012 to December 2022 using all relevant keywords. The first round of data screening produced some basic information, which was further extracted for citation, coupling, co-authorship, co-occurrence, bibliographic, and co-citation analysis. Additionally, a thematic and descriptive analysis was executed to investigate the content of prior research findings, and research techniques used in the literature. The year wise publications indicated that the published literature between 2012 and 2017 was relatively low but however by 2021, a nearly 24-fold increment made it to 154 publications. The three topmost productive journals and conferences included IEEE Transactions on Affective Computing (n = 20 publications) followed by Neurocomputing (n = 17) and Multimedia tools and applications (n = 15). Zhao G was the most proficient author with 48 publications and the top influential country was China (620 publications). Publications by citations showed that each of the authors acquired citations ranging from 100 to 1225. While publications by organizations indicated that the University of Oulu had the most published papers (n = 51). Deep learning, facial expression recognition, and emotion recognition were among the most frequently used terms. It has been discovered that ME research was primarily classified in the discipline of engineering, with more contribution from China and Malaysia comparatively.

Lung Cancer Nodules Detection via an Adaptive Boosting Algorithm Based on Self-Normalized Multiview Convolutional Neural Network.

Lung cancer is the deadliest cancer killing almost 1.8 million people in 2020. The new cases are expanding alarmingly. Early lung cancer manifests itself in the form of nodules in the lungs. One of the most widely used techniques for both lung cancer early and noninvasive diagnosis is computed tomography (CT). However, the intensive workload of radiologists to read a large number of scans for nodules detection gives rise to issues like false detection and missed detection. To overcome these issues, we proposed an innovative strategy titled adaptive boosting self-normalized multiview convolution neural network (AdaBoost-SNMV-CNN) for lung cancer nodules detection across CT scans. In AdaBoost-SNMV-CNN, MV-CNN function as a baseline learner while the scaled exponential linear unit (SELU) activation function normalizes the layers by considering their neighbors' information and a special drop-out technique (α-dropout). The proposed method was trained and tested using the widely Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) and Early Lung Cancer Action Program (ELCAP) datasets. AdaBoost-SNMV-CNN achieved an accuracy of 92%, sensitivity of 93%, and specificity of 92% for lung nodules detection on the LIDC-IDRI dataset. Meanwhile, on the ELCAP dataset, the accuracy for detecting lung nodules was 99%, sensitivity 100%, and specificity 98%. AdaBoost-SNMV-CNN outperformed the majority of the model in accuracy, sensitivity, and specificity. The multiviews confer the model's good generalization and learning ability for diverse features of lung nodules, the model architecture is simple, and has a minimal computational time of around 102 minutes. We believe that AdaBoost-SNMV-CNN has good accuracy for the detection of lung nodules and anticipate its potential application in the noninvasive clinical diagnosis of lung cancer. This model can be of good assistance to the radiologist and will be of interest to researchers involved in the designing and development of advanced systems for the detection of lung nodules to accomplish the goal of noninvasive diagnosis of lung cancer.

Conducting online OSCEs aided by a novel time management web-based system.

BACKGROUND: Objective Structured Clinical Examinations (OSCEs) are an essential part of the assessment process for medical students. They have traditionally been face-to-face assessments, however, due to the COVID-19 pandemic, medical schools have been forced to attempt to carry them out remotely. OSCEs are difficult to carry out online due to rotation management aspects which make it difficult to synchronize movement of students from one station to another. METHODS: The authors have developed a dynamic OSCE time management website which aids in solving the movement synchronization issue. This secure website enables participants to view the list of stations they are allowed to enter, and the start and end time of each station. OSCE administrators can control time of entry and monitor progress of the OSCE remotely. RESULTS: The authors have used the system to conduct several exams successfully, showing the feasibility and cost effectiveness of this method, as well as user acceptance and satisfaction. In contrast to traditional OSCEs, students are set up in individual virtual rooms for the whole exam while examiners, simulated patients and proctors rotate between them. CONCLUSIONS: This online OSCE implementation shows feasibility, cost effectiveness and acceptance of this method. The authors found that student outcomes are comparable to traditional OSCEs conducted in the past. There was no significant difference in student marks in one exam compared to last year, while marks were slightly higher in two exams, potentially due to lack of physical exam stations. An unresolved drawback is the inability to assess physical exam stations online, although having students verbally describe what they would do in physical exam situations may be a partial solution.

A clinical study and treatment results of adrenocortical carcinoma patients presented in Shaukat Khanum Memorial Cancer Hospital and Research Center, Lahore.

Adrenocortical carcinoma is a rare and aggressive malignancy with an incidence of 0.5-2 per million per year and its overall prognosis is very poor. This study was aimed to point out the factors associated with tumour recurrence and patient's survival after complete surgical resection of this aggressive malignancy. Patients admitted from January 2011 till December 2015 were retrospectively reviewed using case notes. In these five years 29 patients were admitted with adrenal tumour out of which 13 had adrenocortical carcinoma. On five year follow up, six (46.2%) patients developed recurrence. MEAN disease free survival was 31.0 ± 23.92 (2-63) months 53.8% while MEAN overall survival was 46.69 ± 22.81 (14-80) months 92.3%. Results concluded that hormonally active tumour on clinical presentation, positive surgical resection margins, positive nodal status and poorly differentiated tumour on histopathology are the factors associated with tumour recurrence and patient's survival.