Statistical testing on generative AI anomaly detection tools in Alzheimer's Disease diagnosis

• URL: http://arxiv.org/abs/2410.13363v1
• Date: Thu, 17 Oct 2024 09:15:09 GMT
• Title: Statistical testing on generative AI anomaly detection tools in Alzheimer's Disease diagnosis
• Authors: Rosemary He, Ichiro Takeuchi,
• Abstract summary: generative AI has shown promise in anomaly detection in medical imaging. We propose to solve this issue with selective inference and develop a reliable generative AI method for Alzheimer's prediction.
• Score: 16.73336092521471
• License:
• Abstract: Alzheimer's Disease is challenging to diagnose due to our limited understanding of its mechanism and large heterogeneity among patients. Neurodegeneration is studied widely as a biomarker for clinical diagnosis, which can be measured from time series MRI progression. On the other hand, generative AI has shown promise in anomaly detection in medical imaging and used for tasks including tumor detection. However, testing the reliability of such data-driven methods is non-trivial due to the issue of double-dipping in hypothesis testing. In this work, we propose to solve this issue with selective inference and develop a reliable generative AI method for Alzheimer's prediction. We show that compared to traditional statistical methods with highly inflated p-values, selective inference successfully controls the false discovery rate under the desired alpha level while retaining statistical power. In practice, our pipeline could assist clinicians in Alzheimer's diagnosis and early intervention.

Related papers

• MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study [0.7751705157998379]
  Alzheimer's disease (AD) and vascular dementia (VaD) are the two most prevalent dementia types. This paper presents an innovative multi-omics approach to accurately differentiate AD from VaD, achieving a diagnostic accuracy of 89.25%.
  arXiv  Detail & Related papers  (2024-11-06T10:13:28Z)
• A Survey of Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
  neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring. As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs. The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
  arXiv  Detail & Related papers  (2024-05-21T06:44:40Z)
• Intelligent Diagnosis of Alzheimer's Disease Based on Machine Learning [24.467566885575998]
  This study is based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. It aims to explore early detection and disease progression in Alzheimer's disease (AD)
  arXiv  Detail & Related papers  (2024-02-13T15:43:30Z)
• Automatic Detection of Alzheimer's Disease with Multi-Modal Fusion of Clinical MRI Scans [8.684668542584701]
  15 million Americans will have either clinical AD or mild cognitive impairment by 2060. We aim to predict the stage of the disease based on two different types of brain MRI scans. We design an AlexNet-based deep learning model that learns the synergy of complementary information from both T1 and FLAIR MRI scans.
  arXiv  Detail & Related papers  (2023-11-30T04:32:28Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance [49.87607548975686]
  The scarcity of labeled data for related diseases poses a huge challenge to an accurate diagnosis. We propose a novel deep reinforcement learning framework, which introduces prior knowledge to direct the learning of diagnostic agents. Our approach's performance was demonstrated using the well-known NIHX-ray 14 and CheXpert datasets.
  arXiv  Detail & Related papers  (2023-06-02T01:46:31Z)
• Early Detection of Alzheimer's Disease using Bottleneck Transformers [1.14219428942199]
  We introduce a novel approach of using an ensemble of the self-attention-based Bottleneck Transformers with a sharpness aware minimizer for early detection of Alzheimer's Disease. The proposed approach has been tested on the widely accepted ADNI dataset and evaluated using accuracy, precision, recall, F1 score, and ROC-AUC score as the performance metrics.
  arXiv  Detail & Related papers  (2023-05-01T16:17:52Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data [75.23250968928578]
  Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
  arXiv  Detail & Related papers  (2020-09-25T01:50:15Z)
• Application of Machine Learning to Predict the Risk of Alzheimer's Disease: An Accurate and Practical Solution for Early Diagnostics [1.1470070927586016]
  Alzheimer's Disease (AD) ravages the cognitive ability of more than 5 million Americans and creates an enormous strain on the health care system. This paper proposes a machine learning predictive model for AD development without medical imaging and with fewer clinical visits and tests. Our model is trained and validated using demographic, biomarker and cognitive test data from two prominent research studies.
  arXiv  Detail & Related papers  (2020-06-02T14:52:51Z)
• Detecting Parkinsonian Tremor from IMU Data Collected In-The-Wild using Deep Multiple-Instance Learning [59.74684475991192]
  Parkinson's Disease (PD) is a slowly evolving neuro-logical disease that affects about 1% of the population above 60 years old. PD symptoms include tremor, rigidity and braykinesia. We present a method for automatically identifying tremorous episodes related to PD, based on IMU signals captured via a smartphone device.
  arXiv  Detail & Related papers  (2020-05-06T09:02:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.