Application of artificial intelligence in brain molecular imaging

Minoshima, Satoshi; Cross, Donna

doi:10.1007/s12149-021-01697-2

Cited by 7 publications

(4 citation statements)

References 58 publications

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“…More in-depth discussions surrounding artificial intelligence (AI) and machine learning (ML) were present across all article types within this review [ 78 , 79 , 80 ]. Zhou et al and Minoshima et al summarized the common ML techniques used in radiology such as feed-forward neural networks (FFNN) and convolutional neural networks (CNN) [ 78 , 79 ]. This work showed that most AI studies in hybrid imaging employ a CNN or generative adversarial network (GAN) as a part of the classification model.…”

Section: Discussionmentioning

confidence: 99%

“…More in-depth discussions surrounding artificial intelligence (AI) and machine learning (ML) were present across all article types within this review [78][79][80]. Zhou et al and Minoshima et al summarized the common ML techniques used in radiology such as feed-forward neural networks (FFNN) and convolutional neural networks (CNN) [78,79].…”

Section: Artificial Intelligencementioning

confidence: 99%

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Current Trends and Applications of PET/MRI Hybrid Imaging in Neurodegenerative Diseases and Normal Aging

Lee,

Renslo,

Wong

et al. 2024

Diagnostics

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Dementia is a significant global health issue that is exacerbated by an aging population. Imaging plays an established role in the evaluation of patients with neurocognitive disorders such as dementia. In current clinical practice, magnetic resonance imaging (MRI) and positron emission tomography (PET) are primary imaging modalities used separately but in concert to help diagnose and classify dementia. The clinical applications of PET/MRI hybrid imaging in dementia are an active area of research, particularly given the continued emergence of functional MRI (fMRI) and amyloid PET tracers. This narrative review provides a comprehensive overview of the rationale and current evidence for PET/MRI hybrid dementia imaging from 2018 to 2023. Hybrid imaging offers advantages in the accuracy of characterizing neurodegenerative disorders, and future research will need to address the cost of integrated PET/MRI systems compared to stand-alone scanners, the development of new biomarkers, and image correction techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Artificial Intelligencementioning

confidence: 99%

Current Trends and Applications of PET/MRI Hybrid Imaging in Neurodegenerative Diseases and Normal Aging

Lee,

Renslo,

Wong

et al. 2024

Diagnostics

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“…Jang et al [17 ▪ ] fused eye movement data and speech data in their multimodal classification model of Alzheimer's disease to obtain a competitive AUC of 0.83. Eye movement data are the promising pathway because it can be captured without sophisticated equipment, possibly even by an untrained individual [18 ▪ ,19 ▪▪ ,20–29,30 ▪▪ ,31,32 ▪ ,33 ▪ ,34,35].…”

Section: Artificial Intelligence Tools For Dementia Diagnosis Using O...mentioning

confidence: 99%

Artificial intelligence in dementia

Richardson

Robbins

Wisely

et al. 2022

Current Opinion in Ophthalmology

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Purpose of reviewArtificial intelligence tools are being rapidly integrated into clinical environments and may soon be incorporated into dementia diagnostic paradigms. A comprehensive review of emerging trends will allow physicians and other healthcare providers to better anticipate and understand these powerful tools. Recent findingsMachine learning models that utilize cerebral biomarkers are demonstrably effective for dementia identification and prediction; however, cerebral biomarkers are relatively expensive and not widely available. As eye images harbor several ophthalmic biomarkers that mirror the state of the brain and can be clinically observed with routine imaging, eye-based machine learning models are an emerging area, with efficacy comparable with cerebral-based machine learning models. Emerging machine learning architectures like recurrent, convolutional, and partially pretrained neural networks have proven to be promising frontiers for feature extraction and classification with ocular biomarkers. SummaryMachine learning models that can accurately distinguish those with symptomatic Alzheimer's dementia from those with mild cognitive impairment and normal cognition as well as predict progressive disease using relatively inexpensive and accessible ocular imaging inputs are impactful tools for the diagnosis and risk stratification of Alzheimer's dementia continuum. If these machine learning models can be incorporated into clinical care, they may simplify diagnostic efforts. Recent advancements in ocular-based machine learning efforts are promising steps forward.

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“…Deep learning methods have gained signi cant traction in various elds, including medicine, natural sciences, computer sciences, technical sciences, and life sciences [1][2][3][4]. Over the past decade, deep learning approaches have been successfully applied in a wide array of elds, such as computed tomography (CT) [5][6][7], magnetic resonance imaging (MRI) [9][10], digital radiography (DR) [10][11][12], positron emission tomography (PET) [13][14][15][16][17], and ultrasound tomography [18][19][20]. Given their widespread success, deep learning methods have been considered for classifying tasks within ultrasound images.…”

Section: Introductionmentioning

confidence: 99%

Transfer Learning with CNNs for Efficient Prostate Cancer and BPH Detection in Transrectal Ultrasound Images

Huang

et al. 2023

Preprint

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Purpose Early detection of prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is crucial for maintaining the health and well-being of aging male populations. This study aims to evaluate the performance of transfer learning with convolutional neural networks (CNNs) for efficient classification of PCa and BPH in transrectal ultrasound (TRUS) images.Methods A retrospective experimental design was employed in this study, with 1,380 TRUS images for PCa and 1,530 for BPH. Seven state-of-the-art deep learning (DL) methods were employed as classifiers with transfer learning applied to popular CNN architectures. Performance indices, including sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), Kappa value, and Hindex (Youden's index), were used to assess the feasibility and efficacy of the CNN methods.Results The CNN methods with transfer learning demonstrated a high classification performance for TRUS images, with all accuracy, specificity, sensitivity, PPV, NPV, Kappa, and Hindex values surpassing 0.9400. The optimal accuracy, sensitivity, and specificity reached 0.9987, 0.9980, and 0.9980, respectively, as evaluated using two-fold cross-validation.Conclusion The investigated CNN methods with transfer learning showcased their efficiency and ability for the classification of PCa and BPH in TRUS images. Notably, the EfficientNetV2 with transfer learning displayed a high degree of effectiveness in distinguishing between PCa and BPH, making it a promising tool for future diagnostic applications.

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Application of artificial intelligence in brain molecular imaging

Cited by 7 publications

References 58 publications

Current Trends and Applications of PET/MRI Hybrid Imaging in Neurodegenerative Diseases and Normal Aging

Current Trends and Applications of PET/MRI Hybrid Imaging in Neurodegenerative Diseases and Normal Aging

Artificial intelligence in dementia

Transfer Learning with CNNs for Efficient Prostate Cancer and BPH Detection in Transrectal Ultrasound Images

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