University:
Boston University
Course:
GMS BC 630 | Ethical & Policy Issues in Health and Medical Services Emergency Management
Academic year:

2024
Views:

435

Pages:

17
Author:

Gironetzbk3n

Machine Learning Powered Medical Image Analytics: Tools Waiting for Applications Medical Applications of ML Matching Engines Patients with similar profile Treatments with similar Cost-Benefit ratio Diagnosis and Treatment Clinical Decision Support Symptoms analyzer Automated Radiology Treatment efficacy Virtual Assistant Smart telemedicine Doctor assistants Workflows Patient flow optimization Detect process inefficiencies Predictive Modeling Risk stratification Healthcare Analytics ML Healthcare Discovery Clinical Trials Hypothesis generation Proof of concept Precision Medicine Genomics Tailored therapies Mobile Apps Well-being Chronic disease management ACIL Activities Framingham Heart Study (PRC) COPDGene •10,000 subjects with HRCT •Genetic epidemiology of COPD • Longitudinal analysis of lung disease •Population-based (3000 subjects) CT Analysis Applied Chest Imaging Laboratory NETT CARDIA • 1,200 with severe COPD • Extensive phenotypic data • Coronary Artery Risk Development in Young Adults •Population based study Clinical Data Why Thoracic Imaging? • • • • • • • • Airways Vasculature Parenchyma Heart Muscle Bone Adipose tissue Bone marrow • • • • • • • Asthma BOS Bronchiectasis CVD COPD ILD Metabolic syndrome • Lung Cancer • PVD/VTE What’s Possible with ML Powered Image Analytics? Image navigation/curation Disease and comorbid condition • Detection • Stratification • Prognostication – Natural history – Therapeutic response (“patients like me”) • Monitoring therapeutic response Organ Detection Onieva, SPIE, 2018 Quantification without Segmentation Bone Mineral Density González G, SPIE 2018 Organ Segmentation RV (blue) and LV (red) Normal Elevated PA pressures with RV dilation Systolic dysfunction and dilation of the LV Acad Radiol. 2017;24(5):594-602. Predicting Clinical Outcomes Acad Radiol. 2017;24(5):594-602. 1157 HRCT of fibrotic lung disease Training: 929, Validation: 89, Test: 139 Walsh et al. Lancet Resp Med 2018. Comparison of algorithm performance to majority vote of 91 specialists Direct Outcome Estimation • Respiratory Events • Hospitalization 32 C3: 8x8x64 Conv +MaxPooling 16 C2: 32x32x32 Conv + MaxPooling C1: 128x128x16 Conv + MaxPooling Input: 512x512x1 FC1: 1024 FC2: {2,#classes,1} 64 González, Am J Respir Crit Care Med, 2017 Path Forward? Opportunities for ML powered image analytics: – Clinical care • Bringing expertise to resource limited areas (ILD, Lung Cancer, etc) – QI – Billing/resource allocation • Challenges: – Model Corruption and Regulatory Review – Who will champion the effort? COPDGene • 10K smokers with a range of lung dysfunction • Volumetric CT scan • 60% NHW, 40% AA Magic? • OR 38.9! • Ethnicity – How did it work? – Well documented differences in body composition/fat free mass – Skeletal structure COPDGene Data Revisited NHW AA