Implementation of AI and quantitative imaging in clinical workflow

Transcription

1 Implementation of AI and quantitative imaging in clinical workflow Angel Alberich-Bayarri, PhD 1 Biomedical Imaging Research Group La Fe Polytechnics and University Hospital 2 QUIBIM SL

2 Outline Introduction why AI algorithms and models are not enough Requirements Implementation Applications Wrap-up

3 Introduction "Quality means doing it right when no one is looking Henry Ford

4 Introduction Doing it right: We all recognize that Artificial Intelligence, Imaging Biomarkers and Smart Data will drive Radiology crucial role in Precision Medicine.

5 Introduction AI & Quantification have still not had an impact in routine Radiology

6 Introduction Can we do it now? Challenge 1: Sort chest X-rays to be reported by the degree of abnormal findings, provided by an AI algorithm embedded in PACS/RIS Challenge 2: Open a MR prostate cancer case for reporting and already having the target lesion segmented with all features extracted (Diffusion, Perfusion and textures), generating a draft PI-RADSv2 report Challenge 3: Search in our PACS or IT system for cases with a CT-derived emphysema percentage higher than 10% to include them in a clinical trial for COPD

7 Introduction Lack of reproducibility: Different quantitative results in workstations from different vendors using the same case Lack of knowledge: For most imaging biomarkers, we still do not know the relationship with clinical endpoints at a large scale (diagnostic, prognostic, treatment response) Lack of IT integration: Lots of research, AI algorithms and start-up companies but few real embedded in radiology workflow

8 Technology is already available Introduction

9 therefore Introduction

10 what is the missing piece? Introduction

11 Introduction

12 Requirements Seamless integration: 1. Cases are retrieved from the PACS automatically by pre-defined rules (i.e. StudyDescription) at specific times (i.e. night?) 2. Pre-computing: A.I. models or automated image analysis pipelines start execution upon reception if a there is a positive match 3. Results are generated and sent back to PACS in order to be ready for radiological reading 4. Although all the process is completely automated, technicians or radiologists can also launch new analysis manually at any time

13 Requirements Requirements for the software platform Alberich-Bayarri A et al. Development of imaging biomarkers and generation of big data. Radiol Med ;122(6):

14 Requirements Requirements vs. Infrastructures Alberich-Bayarri A et al. Development of imaging biomarkers and generation of big data. Radiol Med ;122(6):

15 Requirements Regulatory Imaging Biomarkers Platform = Measurement Instrument = Medical Device class IIa

16 Requirements Data protection (GDPR) Option 1 (Today): Install everything in the hospital without use of Cloud Option 2 (Future): Develop engineering solutions to work mainly with dissociated data in the Cloud (if cloud computing infrastructures are required)

17 System architecture Implementation

18 Mañas-García A. MIUC the new toolkit of QUIBIM Precision platform to beat traditional workstations. quibim.com/blog. 22-OCT-2017 Implementation Hospital connector

19 Implementation

20 Implementation

21 Implementation

22 Applications

23 Quantitative Structured Reports: From images to data Applications

24 Applications Bottlenecks of conventional image analysis: 1. Need for manual delineation of regions of interest (ROI) 2. Lack of help in data interpretation (lack of decision support)

25 Applications Convolutional Neural Networks (CNN): Special type of neural networks which present an outstanding performance in Computer Vision problems. Key elements: Convolutional Layers Pooling Layers Fully Connected Layers In addition to connection weights, convolutional filters are learnt during training, which are able to recognize patterns in images.

26 Applications Train-test process of a CNN Slow (h, d, m) TRAINING GPU Computing Labels Input training data Convolutional Neural Network (CNN) Output (Labels) Weights and filters tuning Fast (s) TESTING Consumer devices Input testing data Previously unseen Tuned Convolutional Neural Network (CNN) Output (Labeled data)

27 Applications The key components for research on AI DATA SCIENTISTS HARDWARE SOFTWARE NVIDIA Quadro GP100 LABELED DATA

28 Applications: Automated region segmentation Mathworks Inc, Natick MA, USA

29 Applications: Automated region segmentation in prostate cancer workflow PERFUSION DIFFUSION ANATOMY Source images Segmentation Feature Organ extraction extraction T2w Volume/Shape features DWI Histogram features Clustering DCE-MRI Nosological image Texture Features Data integration Clinical Genomic Metabolomic Data mining Radiogenomics Predictive/prognostic models Diagnostic models

30 Applications: Automated region segmentation in prostate cancer workflow

31 Applications: Automated region segmentation in prostate cancer workflow AI segmentation (real time video) Human segmentation (x10 accelerated)

32 Applications: Automated region segmentation in prostate cancer workflow Deep Supervision generates output segmentation masks combining multi-layer and multi-resolution information, providing better results than architectures that just use information from the last layer of the network.

33 Applications: Automated region segmentation in Regular UNET prostate cancer workflow Deep Supervision layers generate smoother segmentation masks, therefore closer to the ground truth. Deeply Supervised UNET

34 Applications: Automated region segmentation in prostate cancer workflow Non-supervised clustering ADC K trans Nosologic map Voxelwise clustering Standardized Euclidean

35 Applications: Automated region segmentation in prostate cancer workflow Pathology PI-RADS Nosologic map Pathology PI-RADS Nosologic map (No human involved) Gleason 6 Gleason 7 Gleason 9

36 Applications: Automated region segmentation in other regions MR liver segmentation in diffuse liver diseases (dataset: 150 cases with T1 gradient echo)

37 Applications: Automated region segmentation in other regions Automated vertebra detection and bone analysis (dataset: 230 CT scans)

38 Applications: classification and decision support Chest X-ray abnormality (dataset: chest X-rays) CNN Classifiers Atelectasis Cardiomegaly Effusion Infiltration Mass Nodule Pneumonia Pneumothorax Consolidation Weigthing Fully Connected Network Edema Emphysema Fibrosis Pleural Thickening Abnormal Probability: 0.87 Hernia

39 Applications: classification and decision support Chest X-ray pneumonia detection (RSNA challenge) Blue Boxes Ground Truth (Segmented by an expert radiologist) Red Boxes Artificial Intelligence Predictions

40 Wrap-up It is possible to overcome the lack of integration with innovative platforms covering the requirements for management of AI algorithms and quantitative data This is the key to increase our knowledge in: Relationship between quantitative imaging biomarkers and Clinical Endpoints Precision and accuracy of algorithms and software from vendors

41 Wrap-up

42 Acknowledgements

43 Acknowledgements POST-DOC José Azorín, PhD - Technology Development Alejandro Rodríguez, PhD - Image Analysis Engineer PhD STUDENTS Amadeo Ten - Image Analysis Engineer Sara Carratalá - CNS Analysis CLINICAL TRIALS AND PREBI Sandra Pérez - Data Manager Juan Ramón Terrén - Data Manager Rebeca Maldonado - Technician & PREBI ADMINISTRATION Ana Penadés - Economic & Financial Manager IMAGE ANALYSIS SCIENTISTS Fabio García Castro - Chief Image Analysis Scientist Ana María Jiménez Pastor - Image Analysis Scientist Rafael López González - Image Analysis Scientist Ismael González Valverde Image Analysis Scientist Eduardo Camacho Ramos - Image Analysis Scientist GIBI230 QUIBIM BUSINESS DEVELOPMENT Inmaculada Segarra Granell Business Developer Andrew Tasker - Advisor HUMAN RESOURCES Inmaculada López Human Resources Manager DEVELOPMENT Rafael Hernández Navarro - ChiefTechnologyOfficer Alejandro Mañas García - Full Stack Senior Developer QUALITY AND REGULATORY Belén Fos Guarinos Quality Engineer Ángel Alberich Bayarri, PhD. GIBI Scientific-Technical Director QUIBIM CEO & Founder Luis Martí Bonmatí MD, PhD. GIBI Principal Investigator QUIBIM Founder CLINICAL TRIALS Irene Mayorga Ruíz - ClinicalTrials Coordinator Raúl Yébana Huertas - Image AnalysisTechnician MARKETING AND COMMUNICATION Katherine Wilisch Ramírez - Marketing Manager MANAGEMENT Paula Moreno Ruiz Team Coordinator Encarna Sánchez Bernabé - Chief Operating Officer Kabir Mahajan Chief Strategy Officer

44 Implementation of AI and quantitative imaging in clinical workflow Angel Alberich-Bayarri, PhD 1 Biomedical Imaging Research Group La Fe Polytechnics and University Hospital 2 QUIBIM SL