Diagnostic Performance of Generative Adversarial Network-Based Deep Learning Methods for Alzheimer’s Disease: A Systematic Review and Meta-Analysis

Assistive tools for classifying neurological disorders using fMRI and deep learning: A guide and example

BACKGROUND

Deep-learning (DL) methods are rapidly changing the way researchers classify neurological disorders. For example, combining functional magnetic resonance imaging (fMRI) and DL has helped researchers identify functional biomarkers of neurological disorders (e.g., brain activation and connectivity) and pilot innovative diagnostic models. However, the knowledge required to perform DL analyses is often domain-specific and is not widely taught in the brain sciences (e.g., psychology, neuroscience, and cognitive science). Conversely, neurological diagnoses and neuroimaging training (e.g., fMRI) are largely restricted to the brain and medical sciences. In turn, these disciplinary knowledge barriers and distinct specializations can act as hurdles that prevent the combination of fMRI and DL pipelines. The complexity of fMRI and DL methods also hinders their clinical adoption and generalization to real-world diagnoses. For example, most current models are not designed for clinical settings or use by nonspecialized populations such as students, clinicians, and healthcare workers. Accordingly, there is a growing area of assistive tools (e.g., software and programming packages) that aim to streamline and increase the accessibility of fMRI and DL pipelines for the diagnoses of neurological disorders.

OBJECTIVES AND METHODS

In this study, we present an introductory guide to some popular DL and fMRI assistive tools. We also create an example autism spectrum disorder (ASD) classification model using assistive tools (e.g., Optuna, GIFT, and the ABIDE preprocessed repository), fMRI, and a convolutional neural network.

RESULTS

In turn, we provide researchers with a guide to assistive tools and give an example of a streamlined fMRI and DL pipeline.

CONCLUSIONS

We are confident that this study can help more researchers enter the field and create accessible fMRI and deep-learning diagnostic models for neurological disorders.

A systematic review of generative adversarial networks (GANs) in plastic surgery

INTRODUCTION

Generative adversarial networks (GANs) are a form of deep learning architecture based on the zero-sum game theory, which uses real data to generate realistic fake data. GANs use two opposing neural networks working: a generator and a discriminator. They represent a powerful tool for generating realistic synthetic patient data sets and can potentially revolutionize research. This systematic literature review evaluated the scale and scope of GANs within plastic surgery, constructing a framework for its use and evaluation within subspecialties.

METHODS

Following Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines, a systematic review was performed for applications of GANs in plastic surgery from 2014 to 2022. Three independent reviewers screened from databases: PubMed, Embase, PsychInfo, Scopus, and Google Scholar.

RESULTS

A total of 70 studies were captured by the search, of which seven studies met our criteria. The most common subspecialty was craniofacial (n = 4). Proposed uses of GANs included facial recognition, burn estimation, scar prediction, and post-breast cancer reconstruction anomaly scoring. GANs were conditional, trained on data sets averaging 54,652 ± 112,180 samples, with some sourced publicly and others being primary.

CONCLUSION

GANs hold promise for advancing plastic surgery, backed by diverse applications in the literature. Studies should follow a standardized reporting structure for consistency and transparency, as outlined, especially regarding the data sets used to ensure appropriate representation from an ethnic and cultural diversity perspective. Although GANs require specialist computational expertise to create, surgeons need to understand their development by leveraging the full potential of GANs within the emerging field of computational plastic surgery and beyond.

Neural Computation-Based Methods for the Early Diagnosis and Prognosis of Alzheimer's Disease Not Using Neuroimaging Biomarkers: A Systematic Review

Background

The growing number of older adults in recent decades has led to more prevalent geriatric diseases, such as strokes and dementia. Therefore, Alzheimer's disease (AD), as the most common type of dementia, has become more frequent too.

Background

Objective: The goals of this work are to present state-of-the-art studies focused on the automatic diagnosis and prognosis of AD and its early stages, mainly mild cognitive impairment, and predicting how the research on this topic may change in the future.

Methods

Articles found in the existing literature needed to fulfill several selection criteria. Among others, their classification methods were based on artificial neural networks (ANNs), including deep learning, and data not from brain signals or neuroimaging techniques were used. Considering our selection criteria, 42 articles published in the last decade were finally selected.

Results

The most medically significant results are shown. Similar quantities of articles based on shallow and deep ANNs were found. Recurrent neural networks and transformers were common with speech or in longitudinal studies. Convolutional neural networks (CNNs) were popular with gait or combined with others in modular approaches. Above one third of the cross-sectional studies utilized multimodal data. Non-public datasets were frequently used in cross-sectional studies, whereas the opposite in longitudinal ones. The most popular databases were indicated, which will be helpful for future researchers in this field.

Conclusions

The introduction of CNNs in the last decade and their superb results with neuroimaging data did not negatively affect the usage of other modalities. In fact, new ones emerged.

Predictive models based on machine learning for bone metastasis in patients with diagnosed colorectal cancer

Background

This study aimed to develop an artificial intelligence predictive model for predicting the probability of developing BM in CRC patients.

Methods

From SEER database, 50,566 CRC patients were identified between January 2015 and December 2019 without missing data. SVM and LR models were trained and tested on the dataset. Accuracy, area under the curve (AUC), and IDI were used to evaluate and compare the models.

Results

For bone metastases in the entire cohort, SVM model with poly as kernel function presents the best performance, whose accuracy is 0.908, recall is 0.838, and AUC is 0.926, outperforming LR model. The top three most important factors affecting the model's prediction of BM include extraosseous metastases (EM), CEA, and size.

Conclusion

Our study developed an SVM model with poly as kernel function for predicting BM in CRC patients. SVM model could improve personalized clinical decision-making, help rationalize the bone metastasis screening process, and reduce the burden on healthcare systems and patients.