Neuroimaging studies in people at clinical high risk for psychosis

Gifford, George; McCutcheon, Robert; McGuire, Philip

doi:10.1016/b978-0-12-813201-2.00009-0

Cited by 4 publications

(3 citation statements)

References 110 publications

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“…The common method used for processing sMRI is voxel-based morphometry, it can also be used to assess the degree of cortical folding or pattern and variation of cortical gyrification. sMRI has the advantages of clear interpretation, early implemented across centres and relatively low cost over fMRI, electroencephalography and proton magnetic resonance spectroscopy 23 (Figure 8).…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor

MO,

2023

MSEIJ

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Background: Brain is the control center of the human body, in recent time, different variety of brain diseases are being discovered. The brain disease diagnosis tools are becoming challenging and still an open area of research, application of AI in brain disease diagnosis has made disease prediction and detection more precise and accurate. Automated technologies for non-invasive analysis of brain images have become necessary, because disease of brain is fatal and are the cause of large number of deaths in developed countries. Brain tumor surgery augmented with AI can result in safer and more effective treatment. The knowledge gap between clinical and data science experts still presents significant challenges. This paper will review literatures related to current challenges of AI technologies for brain tumor diagnosis and suggest new directions of AI technologies for diagnosing brain tumour. A systematic search of major academic databases (such as Science Direct, IEEE explore digital Library, and Google scholar) was conducted to identify relevant studies published between 2015 and 2023. The search term used in this study include “Brain tumor Diagnosis”, “AI challenges in Brain tumor Diagnosis”, ‘AI techniques” and “AI challenges in medicine and future”. Studies were included if they utilized AI techniques for brain tumor diagnosis. The identified studies were evaluated for the key challenges they encountered in their diagnostic approaches. The Present study identified several challenges related to the application of AI techniques in brain tumour diagnosis. These challenges include: Interpretability and explainability, variations in tumour location, shape, and size which make accurate segmentation and classification difficult. Overall, the challenges in explaining brain tumor detection stem from the unique requirements and complexities of the healthcare domain, necessitating specialized techniques and approaches. This study summarizes the new directions for AI as (I) Data Hungry: Large, standardized, annotated data sets and excellent ground truth data are necessary for the development of accurate AI. (II) Radiomics: makes it possible to extract a vast number of quantitative features from intricate clinical imaging arrays and convert them into high-dimensional data that can be further processed to determine their relationship to the histological features of the tumor, which represent underlying genetic mutations and malignancy as well as grade, progression, response to therapy, and even overall survival (OS). (III) Black box: AI, for instance, is capable of predicting the best course of care for a patient, but it is unable to explain its reasoning. A trend toward easing this restriction is interpretable deep learning, (IV) Demonstrating the generalizability of deep learning applications and conducting external validation are two major obstacles. (V) There are knowledge gaps in clinical oncology that need to be filled in order to successfully integrate AI and maximize its effects. (VI) Several national professional bodies have started programs to bridge these knowledge gaps and advance the adoption of AI in oncology in response to these difficulties.

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Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor

MO,

2023

MSEIJ

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“…Many imaging techniques have emerged for AD diagnosis such as structural magnetic resonance imaging (sMRI) scans, shown in Figure, which is one of neuroimaging techniques used to support doctors' diagnosis of AD and help measure the size of degeneration of some brain areas that can lead to early detection [31]. sMRI scans , shown in Fig 3, produce high contrast images used to measure the volume of the grey and white matter of the patient's brain [32]. They can detect the atrophic alterations in the human brain that could lead to severe damages causing AD [33].…”

Section: Image Biomarkersmentioning

confidence: 99%

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Alqubati¹,

Algaphari²

2021

IJSECS

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It can cause a massive impact on a patient's memory and mobility. As this disease is irreversible, early diagnosis is crucial for delaying the symptoms and adjusting the patient's lifestyle. Many machine learning (ML) and deep learning (DL) based-approaches have been proposed to accurately predict AD before its symptoms onset. However, finding the most effective approach for AD early prediction is still challenging. This review explored 24 papers published from 2018 until 2021. These papers have proposed different approaches using state of the art machine learning and deep learning algorithms on different biomarkers to early detect AD. The review explored them from different perspectives to derive potential research gaps and draw conclusions and recommendations. It classified these recent approaches in terms of the learning technique used and AD biomarkers. It summarized and compared their findings, and defined their strengths and limitations. It also provided a summary of the common AD biomarkers. From this review, it was found that some approaches strove to increase the prediction accuracy regardless of their complexity such as using heterogeneous datasets, while others sought to find the most practical and affordable ways to predict the disease and yet achieve good accuracy such as using audio data. It was also noticed that DL based-approaches with image biomarkers remarkably surpassed ML based-approaches. However, they achieved poorly with genetic variants data. Despite the great importance of genetic variants biomarkers, their large variance and complexity could lead to a complex approach or poor accuracy. These data are crucial to discover the underlying structure of AD and detect it at early stages. However, an effective pre-processing approach is still needed to refine these data and employ them efficiently using the powerful DL algorithms.

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“…An important goal in systems neuroscience is to understand how variation in brain structure relates to individual differences in behavior (Genon et al, 2022). Structural T1-weighted magnetic resonance imaging (MRI) is a non-invasive technique for examining the anatomy of the human brain, providing high contrast between gray and white matter (Gifford et al, 2020). Structural MRI is widely used to predict behavioral traits, clinical symptoms and diagnostic categories in both healthy individuals and individuals with neuropsychiatric disorders (Sabuncu et al, 2015; Arbabshirani et al, 2017; Bhagwat et al, 2019; Cohen et al, 2021; Ooi et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

Wulan,

An,

Zhang

et al. 2024

Preprint

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Individualized phenotypic prediction based on structural MRI is an important goal in neuroscience. Prediction performance increases with larger samples, but small-scale datasets with fewer than 200 participants are often unavoidable. We have previously proposed a “meta-matching” framework to translate models trained from large datasets to improve the prediction of new unseen phenotypes in small collection efforts. Meta-matching exploits correlations between phenotypes, yielding large improvement over classical machine learning when applied to prediction models using resting-state functional connectivity as input features. Here, we adapt the two best performing meta-matching variants (“meta-matching finetune” and “meta-matching stacking”) from our previous study to work with T1-weighted MRI data by changing the base neural network architecture to a 3D convolution neural network. We compare the two meta-matching variants with elastic net and classical transfer learning using the UK Biobank (N = 36,461), Human Connectome Project Young Adults (HCP-YA) dataset (N = 1,017) and HCP-Aging dataset (N = 656). We find that meta-matching outperforms elastic net and classical transfer learning by a large margin, both when translating models within the same dataset, as well as translating models across datasets with different MRI scanners, acquisition protocols and demographics. For example, when translating a UK Biobank model to 100 HCP-YA participants, meta-matching finetune yielded a 136% improvement in variance explained over transfer learning, with an average absolute gain of 2.6% (minimum = -0.9%, maximum = 17.6%) across 35 phenotypes. Overall, our results highlight the versatility of the meta-matching framework.

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Neuroimaging studies in people at clinical high risk for psychosis

Cited by 4 publications

References 110 publications

Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor

Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Translating phenotypic prediction models from big to small anatomical MRI data using meta-matching

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