A Systematic Review: Deep Learning based E-Learning Recommendation System

2021 ◽  
Author(s):  
Roshan Bhanuse ◽  
Sandip Mal
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Recommendation System ◽  
E Learning

Are e-learning Webinars the future of medical education? An exploratory study of a disruptive innovation in the COVID-19 era

2020 ◽  
pp. 1-10
Author(s):  
Colin J. McMahon ◽  
Justin T. Tretter ◽  
Theresa Faulkner ◽  
R. Krishna Kumar ◽  
Andrew N. Redington ◽  
...  
Keyword(s):  
Deep Learning ◽  
Carbon Footprint ◽  
Quantitative Research ◽  
Survey Design ◽  
Hybrid Approach ◽  
Disruptive Innovation ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
E Learning ◽  
The Impact

Abstract Objective: This study investigated the impact of the Webinar on deep human learning of CHD. Materials and methods: This cross-sectional survey design study used an open and closed-ended questionnaire to assess the impact of the Webinar on deep learning of topical areas within the management of the post-operative tetralogy of Fallot patients. This was a quantitative research methodology using descriptive statistical analyses with a sequential explanatory design. Results: One thousand-three-hundred and seventy-four participants from 100 countries on 6 continents joined the Webinar, 557 (40%) of whom completed the questionnaire. Over 70% of participants reported that they “agreed” or “strongly agreed” that the Webinar format promoted deep learning for each of the topics compared to other standard learning methods (textbook and journal learning). Two-thirds expressed a preference for attending a Webinar rather than an international conference. Over 80% of participants highlighted significant barriers to attending conferences including cost (79%), distance to travel (49%), time commitment (51%), and family commitments (35%). Strengths of the Webinar included expertise, concise high-quality presentations often discussing contentious issues, and the platform quality. The main weakness was a limited time for questions. Just over 53% expressed a concern for the carbon footprint involved in attending conferences and preferred to attend a Webinar. Conclusion: E-learning Webinars represent a disruptive innovation, which promotes deep learning, greater multidisciplinary participation, and greater attendee satisfaction with fewer barriers to participation. Although Webinars will never fully replace conferences, a hybrid approach may reduce the need for conferencing, reduce carbon footprint. and promote a “sustainable academia”.

scholarly journals A Systematic Review of the DeLone and McLean Model of Information Systems Success in an E-Learning Context (2010–2020)

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Hala Najwan Sabeh ◽  
Mohd Heikal Husin ◽  
Daisy Mui Hung Kee ◽  
Ahmad Suhaimi Baharudin ◽  
Rosni Abdullah
Keyword(s):  
Systematic Review ◽  
Information Systems ◽  
Learning Context ◽  
Information Systems Success ◽  
Delone And Mclean ◽  
E Learning

scholarly journals Deep learning for cephalometric landmark detection: systematic review and meta-analysis

2021 ◽  
Author(s):  
Falk Schwendicke ◽  
Akhilanand Chaurasia ◽  
Lubaina Arsiwala ◽  
Jae-Hong Lee ◽  
Karim Elhennawy ◽  
...  
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Meta Analysis ◽  
High Accuracy ◽  
Risk Of Bias ◽  
Automated Detection ◽  
Reference Test ◽  
Landmark Detection ◽  
Future Studies ◽  
Using Data

Abstract Objectives Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs. Methods Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498). Data From 321 identified records, 19 studies (published 2017–2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7–93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (–0.581; 95 CI: –1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824). Conclusions DL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed. Clinical significance Existing DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective.

scholarly journals Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shelly Soffer ◽  
Eyal Klang ◽  
Orit Shimon ◽  
Yiftach Barash ◽  
Noa Cahan ◽  
...  
Keyword(s):  
Systematic Review ◽  
Pulmonary Embolism ◽  
Deep Learning ◽  
Sensitivity And Specificity ◽  
Meta Analysis ◽  
Risk Of Bias ◽  
Future Research ◽  
Learning Models ◽  
Summary Receiver Operating Characteristic ◽  
Pulmonary Angiogram

AbstractComputed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

scholarly journals Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 667
Author(s):  
Wei Chen ◽  
Qiang Sun ◽  
Xiaomin Chen ◽  
Gangcai Xie ◽  
Huiqun Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Systematic Review ◽  
Deep Learning ◽  
Heart Sounds ◽  
Learning Approaches ◽  
Automated Classification ◽  
Learning Methods ◽  
Artificial Intelligence Technology ◽  
Medical Big Data ◽  
Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

scholarly journals Automatic image annotation based on deep learning models: a systematic review and future challenges

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Myasar Mundher Adnan ◽  
Mohd Shafry Mohd Rahim ◽  
Amjad Rehman ◽  
Zahid Mehmood ◽  
Tanzila Saba ◽  
...  
Keyword(s):  
Systematic Review ◽  
Deep Learning ◽  
Image Annotation ◽  
Automatic Image Annotation ◽  
Learning Models ◽  
Future Challenges
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