from qualitative to quantitative radiological reports...
TRANSCRIPT
Quantitative Imaging Biomarkers in Medicine
www.quibim.com
Ana Jiménez PastorImage Analysis Scientist
From Qualitative to Quantitative Radiological
Reports through AI and Automated Analysis
ANALOGIC MEDICAL IMAGE DIGITAL MEDICAL IMAGE
Thanks to digitalization, medical imaging is one of the medical sectors which has
experienced a huge technological revolution in the last 50 years
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Moderate Lung Emphysema 20% Lung Emphysema
DISEASE
vs.
HUMAN EYE
1896. William Morton
First Radiological Report: RX Abdomen
“… there is no stone of appreciable size in the kidney…”
2018. Radreport. RSNA
Modern Radiological Report: CT kidney stones
“… adenopathy is not observed…”
“There are not observed alterations”
The need: TO MEASURE
Steatohepatitis Alzheimer Articular degeneration Vertebral fractures Prostate cancer
Chondral degeneration Rectal cancer Emphysema Multiple SclerosisOsteoporosis
“When you can measure what you are
speaking about, and express it in
numbers, you know something about it”
Lord Kelvin
What is an Imaging Biomarker?
It is a parameter obtained objectively from medical images which represents and quantifies a tissue property (structural,
functional or biological) extracted after applying computational models and artificial intelligence,
to measure is to know
Imaging Biomarkers Development
Value
Proof of concept Proof of mechanism Image acquisition Image processing Image analysis
Measurements Proof of principleProof of efficacy
and effectivenessStructured report
Idea
Martí-Bonmati ́ L, Alberich-Bayarri A, et al. Imaging biomarkers, quantitative imaging, and bioengineering. Radiologia. 2012;54:269-278.
ONCOLOGYSolid tumors, hematology
Diffusion (IVIM, DKI),
Pharmacokinetics, bone
narrow infiltration,
multiparametric analysis
MSKOsteoporosis, arthrosis
Trabecular morphology,
fractal irregularity,
anisotropy, mechanical
resistence and elasticity
LUNGCOPD, RX
Classification, volumes,
emphysema, low functional
densities
NEUROAlzheimer, ALS, ME, ictus
Brain volumetry, cortical
thickness, White matter
lesions, tractography
LIVERSteatohepatitis, fibrosis,
hemochromatosis
Fat fraction, iron
concentration, fibrosis
Why do we need HPC?
HIGH DIMENSIONALITY!
2688x2672
512x512x1000
192x192x1300
COMPLEXITY IN AI ARCHITECTURES!
HIGH COMPUTATIONAL COST!
We need HPC integrated in
healthcare and medical imaging!
vs.
Which HPC installation does fit the best in the
healthcare sector?
AI IN MEDICAL IMAGING
LIVER FAT & IRON QUANTIFICATION
Nowadays, liver biopsy is the gold standard:
• Invasive
• Heterogeneous distribution – Biased sample
• Intra- and inter- observer variability
• Difficulties on patients follow-up
SOLUTION: Virtual biopsies through MRI
• Requires the delineation of the liver.
• Time consuming, exhausting and not
rewarding task.
• Requires radiologists’ expert
knowledge, therefore it is very
expensive.
LIVER FAT & IRON QUANTIFICATION
SOLUTION: Automatic liver segmentation using a CNN
SEGMENTATION. LIVER
AI VS HUMAN SEGMENTATION
AI SEGMENTATION HUMAN SEGMENTATION
DETECTION. VERTEBRAE
• Vertebra localization and identification are essential for
diagnosis, surgical planning and patients follow-up on spine
pathologies.
• Nowadays, this identification is done manually, hindering
radiologists’ workflow.
• SOLUTION: Automatic vertebrae detection through AI.
DETECTION. VERTEBRAE
DETECTION IDENTIFICATION
DETECTION. VERTEBRAE
DETECTION. PNEUMONIA
• Pneumonia accounts for over 15% of all deaths of children
under 5 years old internationally.
• The diagnosis on chest x-ray is complicated because of the
presence of other conditions in the lungs.
• SOLUTION: Pneumonia detection to improve efficiency and
reach of diagnostic services.
DETECTION. PNEUMONIA
Ground truth
AI Predictions
CLASSIFICATION. CHEST X-RAY
• X-ray is the most performed examination.
• Radiology departments have limitations in reporting all x-rays
generated due to the limited resources available.
• SOLUTION: Automatic chest x-ray classification to prioritize
those with abnormal findings.
CLASSIFICATION. CHEST X-RAY
Atelectasis
Cardiomegaly
Effusion
Infiltration
Mass
Nodule
Pneumonia
Pneumothorax
Consolidation
Edema
Emphysema
Fibrosis
Pleural
Thickening
Hernia
Abnormal Probability:
0.87
Weigthing
Fully Connected
Network
CNN Classifiers
RADIOMICS
• Multivariate analysis of large datasets of imaging
biomarkers with the goal of finding diagnostic, prognostic
or therapeutic relations.
• It is based on the analysis of large populations
characteristics to extract correlations that can be applied
to an individual patient.
RADIOMICS
Powerful GPUs are a must to develop
AI because of their high parallel
computing requirements
OUR
TEAM
Raúl Yébana
Huertas
Image Analysis
Technician
Andrew Tasker
Business
Development
Human Resources
Management
Inma López
Image Analysis
Scientist
Fabio García
Castro
CTO
Rafael
Hernández
Navarro
Alejandro Mañas
García
Full Stack
Senior Developer
Image Analysis
Scientist
Eduardo
Camacho
Irene Mayorga
Ruiz
Clinical Trials
Coordinator
Ana Jiménez
Pastor
Image Analysis
Scientist
Image Analysis
Scientist
Ismael González
Belén Fos
Guarinos
Quality Engineer
ManagerCOO
Encarna
Sánchez
Bernabé
Marketing
Manager
Katherine
WilischRamírez
Data Scientist
Rafael López
Inma Segarra
Business
DevelopmentFounder & CEO
Ángel
Alberich-Bayarri
Chief Strategy
Officer
Kabir Mahajan
Team Coordinator
Paula Moreno
AI
Quantitative Imaging Biomarkers in Medicine
www.quibim.com
Ana Jiménez PastorImage Analysis Scientist
From Qualitative to Quantitative Radiological
Reports through AI and Automated Analysis