Deep Learning Applied to Electroencephalogram Data in Mental Disorders: A systematic Review

Background: Epigenetic modifications appear to be dynamic and they might be affected by environmental factors. The possibility of influencing these processes through psychotherapy has been suggested. Objective: To analyse the impact of psychotherapy on epigenetics when applied to mental disorders. The main hypothesis is that psychological treatments will produce epigenetic modifications related to the improvement of treated symptoms. Methods: A computerised and systematic search was completed throughout the time period from 1990 to 2019 on the PubMed, ScienceDirect and Scopus databases. Results: In total, 11 studies were selected. The studies were evaluated for the theoretical framework, genes involved, type of psychotherapy and clinical challenges and perspectives. All studies showed detectable changes at the epigenetic level, like DNA methylation changes, associated with symptom improvement after psychotherapy. Conclusion: Methylation profiles could be moderating treatment effects of psychotherapy. Beyond the detected epigenetic changes after psychotherapy, the epigenetic status before the implementation could act as an effective predictor of response.

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The effect of preventative interventions on the mental health of children considered at risk for future mental disorders: a systematic review.

The JBI Database of Systematic Reviews and Implementation Reports ◽

10.11124/01938924-200604101-00003 ◽

2006 ◽

Vol 4 (Supplement) ◽

pp. 1-10

Keyword(s):

Mental Health ◽

Systematic Review ◽

At Risk ◽

Mental Disorders ◽

Preventative Interventions

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Validated Screening Tools for Common Mental Disorders in Low and Middle Income Countries: A Systematic Review

PLoS ONE ◽

10.1371/journal.pone.0156939 ◽

2016 ◽

Vol 11 (6) ◽

pp. e0156939 ◽

Cited By ~ 82

Author(s):

Gemma-Claire Ali ◽

Grace Ryan ◽

Mary J. De Silva

Keyword(s):

Systematic Review ◽

Mental Disorders ◽

Common Mental Disorders ◽

Screening Tools ◽

Middle Income ◽

Middle Income Countries ◽

Low And Middle Income

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

Clinical Oral Investigations ◽

10.1007/s00784-021-03990-w ◽

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.

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

Scientific Reports ◽

10.1038/s41598-021-95249-3 ◽

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.

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Pre and Probiotics Involved in the Modulation of Oral Bacterial Species: New Therapeutic Leads in Mental Disorders?

Microorganisms ◽

10.3390/microorganisms9071450 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1450

Author(s):

Yoann Maitre ◽

Rachid Mahalli ◽

Pierre Micheneau ◽

Alexis Delpierre ◽

Marie Guerin ◽

...

Keyword(s):

Systematic Review ◽

Central Nervous System ◽

Nervous System ◽

Oral Health ◽

Mental Disorders ◽

Meta Analysis ◽

Bacterial Species ◽

Oral Microbiome ◽

Oral Microbiota ◽

Therapeutic Leads

This systematic review aims to identify probiotics and prebiotics for modulating oral bacterial species associated with mental disorders. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guideline, we search the electronic MEDLINE database published till January 2021 to identify the studies on probiotics and/or prebiotics for preventing and treating major oral dysbiosis that provokes mental disorders. The outcome of the search produces 374 records. After excluding non-relevant studies, 38 papers were included in the present review. While many studies suggest the potential effects of the oral microbiota on the biochemical signalling events between the oral microbiota and central nervous system, our review highlights the limited development concerning the use of prebiotics and/or probiotics in modulating oral dysbiosis potentially involved in the development of mental disorders. However, the collected studies confirm prebiotics and/or probiotics interest for a global or targeted modulation of the oral microbiome in preventing or treating mental disorders. These outcomes also offer exciting prospects for improving the oral health of people with mental disorders in the future.

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Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽

10.3390/e23060667 ◽

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.

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