ophthalmology personalized healthcare program - ois.net · genentech/roche robust ophthalmology...

Ophthalmology Personalized Healthcare Program

Daniela Ferrara MD PhD

Senior Medical Director

Personalized Health Care

Clinical Science Ophthalmology

Genentech, Roche

Presented at the OIS Ophthalmology Innovation Summit | July 25, 2019 | Chicago, IL

Deep Learning

Advanced Analytics

Clinical

Imaging

Meaningful Data at Scale Vision Loss Prevention

Treat intermediate

disease early…

Personalized Therapy

Faricimab

Port

Delivery

System…To prevent

irreversible vision loss

‘Omics

Ranibizumab ITV

Goal to Treat Vision Loss and Preserve Vision

Real

World


To Predict and Prevent Vision Loss

Meaningful

data at scale

Advanced

analytics

Smarter

faster

efficient

R&D

Personalized

care

& Improved

access

Impact

Digital

PathologyGenomicsDigital HealthReal World Data

Electronic

Medical RecordsImaging


From Data to Analytics to Impact: Begins and Ends with the Patient

Algorithm Development Strategy

Real-world Performance Monitoring & Retraining

Train & TuneAlgorithms

Prototype

algorithms

on legacy data

Refine & EvolveAlgorithms

Mature

algorithms

on larger external

or real-world data

ValidateAlgorithms

DeployAlgorithms

Algorithms on

prospective

clinical data

Potential Intended Uses

Incre

asin

g C

om

ple

xity

For an individual patient:

Disease Detection Tool

Detects disease

Risk Prediction Tool

Provides risk prediction of disease conversion

Treatment Recommendation Tool

Recommends best management option,

either monitoring or treatment

Prevention Tool

Predicts disease conversion early enough to

recommend prophylactic treatment


Algorithm Development Pathway

Engaging with

regulators,

Health care

providers and

payers

1. Smith AF. Br J Ophthalmol. 2010;94:1116-7

2. International Diabetes Federation. IDF Diabetes Atlas. Eighth edition 2017. https://www.idf.org/e-library/welcome.html. Accessed January 9, 2019.

Global prevalence of diabetes is projected to increase by ~50% in 20452

2017: 425 million 2045: 629 million


Addressing High Unmet Medical Needs

Global projected prevalence of neovascular age-related macular degeneration

2010: 23 million 2050: 80 million1

1. https://www.idf.org/e-library/welcome.html. Accessed January 9, 2019. 2. Bressler NM et al. JAMA Ophthalmol. 2014;132(2):168-173. 3. Willis JR et al. Ophthalmic Epidemiol. 2018;25(5-6):365-372. 4.

Fenner BJ et al. Ophthalmol Ther. 2018;7(2):333-346. 5. Tran K et al. Curr Opin Ophthalmol. 2018;29(6):566-575. 6. Zimmer-Galler IE et al. Curr Opin Ophthalmol. 2015;26(3):167-172.

DME, diabetic macular edema; DR, diabetic retinopathy.

Optical Coherence Tomography (OCT)4-6

✓ 3D volume at the central retina: requires specialized acquisition

✓ Current gold standard for DME diagnosis

But… Modality is not always available for tele-ophthalmology

✓ 2D image of the retina: easy to be acquired✓ DR screening is currently largely based o CFP But… DME screening is limited in this modality

Color Fundus Photography (CFP)4-6


Challenges in Screening for Diabetic Macular Edema

Normal Diabetic Macular Edema

Many individuals with Diabetes are not getting screened or receiving care that can prevent

visual impairment and blindness1-3

Nat Biomed Eng. 2018;2(2):158-164.

JAMA. 2016;316(22):2402-2410.

JAMA Ophthalmol. 2017;135(11):1170-1176.

Invest Ophthalmol Vis Sci. 2018;59(8):3199-3208.

ARTIFICIAL INTELLIGENCE

A program that aims to simulate

human intelligence

MACHINE LEARNING

An AI process in which the

performance of algorithms

improves over time with more

data exposure

DEEP LEARNING

Subset of machine learning

that uses multiple layers of

neural networks to learn

from a large volume of data

1. Hogarty DT et al. Clin Exp Ophthalmol. 2018 Aug 28 [Epub ahead of print].

AI, artificial intelligence; AMD, age-related macular degeneration.


Deep Learning: Machine Learning for Knowledge Discovery

Deep learning offers a novel methodology to address scientific questions,

but its interpretability represents an open challenge1

1. Ting DSW et al. Br J Ophthalmol. 2019;103(2):167-175.

Input Processing Output

Answer to a

scientific

question

Raw Image Convolutional Neural Network


Deep Learning: Potential for Macular Thickness Assessment

Transfer learning cascade is a powerful strategy to make deep learning feasible in a real-world setting

1. Russakovsky O et al. Int J Comput Vis. 2015;115(3):211-252. 2. Kaggle. Diabetic retinopathy detection. 2017. https://www.kaggle.com/c/diabetic-retinopathy-detection. Accessed January 31, 2019.

CST, central subfield thickness; DR, diabetic retinopathy; OCT, optical coherence tomography (*time-domain).

Network learns to recognize

natural images

Network specializes in

understanding fundus photos

Transfer Learning from ImageNet1

Transfer of “Knowledge”

KaggleDR Color

Fundus Photo Dataset2

KaggleDR challenge:

is it severe DR?

Inception-V3

YES

NO

CST 400 µm (Yes vs No)?

CST 250 µm (Yes vs No)?

Actual CST Value?

RIDE/RISE Color Fundus Photo Dataset

Deep learning (Inception-V3 architecture)

Convolution

Pooling

Softmax

Other

Convolution

Pooling

Softmax

Other


Transfer Learning Cascade Strategy in Deep Learning

Deep learning applied on 17,997 fundus

photos from RIDE & RISE phase 3

DME trials to predict OCT-equivalent*

measures of macular thickening

1. Arcadu F, Benmansour F, Maunz A, et al. Deep learning predicts OCT measures of diabetic macular thickening from color fundus photographs. Invest Ophthalmol Vis Sci. 2019;60:852-857.

CST, central subfield thickness; AUC, area under the curve; n, number of cases; OCT, optical coherence tomography (time-domain); OP, operating point.

CST 250 µm

AUC 95% CI n

All validation samples 0.86 0.81–0.90 307

High-quality color fundus

photos without laser scars0.97 0.89–1.0 24

CST 400 µm




AUC: 0.86

Sensitivity OP: 80.3

Specificity OP: 78.9

Central Subfield Thickness (CST) 250 µm

(All Validation Samples)

The performance of the model increased with algorithm training

on fundus photos of high quality and without laser scars

Deep learning model successfully identified color fundus photos with Diabetic Macular Edema

and can support broader screening efforts


Pilot Project on Diabetic Retinopathy

Central Subfield Thickness (CST)

R2 95% CI n




CST 250 µm CST 400 µm

1. Arcadu F, Benmansour F, Maunz A, et al. Deep learning predicts OCT measures of diabetic macular thickening from color fundus photographs. Invest Ophthalmol Vis Sci. 2019;60:852-857.


Pilot Project on Diabetic Retinopathy

Attribution maps highlighting “hot spots” on fundus photos providing insight into:

• What the algorithm “sees”

• How the deep learning model develops its output

• Biological plausibility of features identified by the model


Pilot Project on Neovascular Age-Related Macular Degeneration

Machine learning algorithm predicted response to anti-VEGF treatment and visual outcomes

and can support physician’s decision for clinical management


Pilot Project on Geographic Atrophy

Assessment of potential prognostic variables in GA lesion occurrence and progression

and can enable research and development with smarter clinical trials

Genentech/Roche robust ophthalmology pipeline, rich longitudinal datasets,

innovative partners and assets, and strong analytics capabilities supports our program and

invites strong collaboration with the retina community and other expert leaders in the field

Ranibizumab ITV

Faricimab Port Delivery System

Robust ophthalmology pipeline

with new drug targets

and delivery systems

~10k patients and ~3M

fundus images

from legacy trialsUnique assets and

collaborations


Collaborations to Achieve Meaningful Results for Patients

ophthalmology personalized healthcare program - ois.net · genentech/roche robust ophthalmology...

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