BOSTON, March 31, 2020 /PRNewswire/ -- Artificial
intelligence (AI) is revolutionizing medical diagnostics. The
state-of-the-art results have already demonstrated that software
can achieve fast and accurate image-based diagnostics on various
conditions affecting the skin, eye, ear, lung, breast, and so on.
These technological advancements can help automate the diagnosis
and triage processes, accelerating the process to speed up the
referral process especially in urgent cases, freeing up expert
resources, offering the best accuracy everywhere regardless of
skill levels, and making the processes more widely available. This
is a ground-breaking development with far-reaching consequences.
Naturally, many innovators are scrambling to capitalize on these
advancements.
The report "Digital Health & Artificial Intelligence 2020:
Trends, Opportunities, and Outlook" from emerging technology
research firm IDTechEx, has examined this trend. This report
considers the trend towards digital and AI applications in health.
It outlines the state-of-the-art in AI-based diagnosis of various
conditions affecting the skin, eye, heart, breast, brain, lung,
blood, genetic disorders and so on. The data sources employed are
diverse including dermoscopic images, fundus images, OCT, CT, CTA,
echocardiograms, electrocardiogram, mammography, pathology slides,
low-res mobile phone pictures and more. This report then identifies
and highlights companies seeking to capitalize on these technology
advances to automate the diagnostic and triage process.
Furthermore, this report considers the trend of digital health
more generally. It provides a detailed overview of the ecosystem
and offers insights into the key trends, opportunities and
outlooks for all aspects of digital health,
including: Telehealth and telemedicine, Remote patient
monitoring, Digital therapeutics / digiceuticals / software as a
medical device, Diabetes management, Consumer genetic testing,
Smart home as a carer and AI in diagnostics.
Ground-breaking technology
Significant funding is flowing to start-ups and R&D teams of
large corporations who develop AI tools to accelerate and/or
improve the detection and classification of various diseases based
on numerous data sources ranging from RGB images to CT scans, ECG
signals, mammograms and to pathological slides. The
state-of-the-art results demonstrate that software can do these
tasks faster, cheaper, and often more accurately than trained
experts and professionals.
This is an important development which, if successful, can have
far-reaching consequences: it can make diagnostics much more widely
available and it can free up medical experts' time to focus on more
complex tasks which currently sit beyond the capabilities of
AI-based automation. The technology is today making leaps forward,
but technology is only a piece of the puzzle, and many other
challenges will need to be overcome before such software tools are
widely adopted. However, the direction of travel is clear.
This trend is today on the rise because (a) the availability of
digitized medical data sources is rapidly increasing, offering
excellent algorithm training feedstock, and (b) advancements in AI
algorithms specially trained deep neural networking are enabling
software to tackle tasks which it hitherto could not do.
The IDTechEx report "Digital Health & Artificial
Intelligence 2020: Trends, Opportunities, and Outlook" outlines
many such advancements and identifies some of the key companies
pursuing each opportunity. The remainder of this article briefly
outlines two specific cases: eye disease and skin disease.
Eye Disease
Diabetic retinopathy is a complication that affects the eye.
Researchers from India have
recently shown that the software accurately interprets retinal
fundus photographs to enable a large-scale screening program to
detect diabetic retinopathy. The software is trained to make
multiple binary classifications, allocating a risk level to each
patient. The algorithm was trained and tuned on a total of more
than 140k images. The machine matched
and exceeded the sensitivity and selectivity level achieved by
trained manual experts. The software achieved 92.1% and 95.2%
sensitivity and selectivity, respectively.
Naturally, there is a strong business case here, and many are
seeking to capitalize on it. One example is IDx, based out of
Iowa in the US, who has designed
and developed an algorithm to detect diabetic retinopathy. Their AI
system achieves a sensitivity and specificity of 87% and 90%,
respectively. In as early as 2017, it was tested at 10 sites across
the US on 900 patients.
A very insightful test in eye clinics is the OCT (optical
coherence tomography), which creates high-resolution (5um) 3D maps
of the back of the eye and require expert analysis to interpret.
OCT is now one of the most common imaging procedures with 5.35
million OCT scans performed in the US Medicare population in 2014
alone. This creates a backlog in processing and triage, and such
delays can be harmful when they cause avoidable treatment delay for
urgent cases.
DeepMind (Google) has demonstrated an algorithm that can
automate the triage process based on 3D OCT image. Their algorithm
design has some unique features. It consists of two stages: (1) a
segmentation network and (2) a classification network. The first
network will output a labelled tissue segmentation map. Based on
the segmented maps, the second network will output a diagnosis
probability for over 50 eye-threatening eye conditions and provide
referral suggestion. The first part was trained on 877 sparely and
manually segmented images and the second network on 14,884 training
tissue maps with confirmed diagnosis and referral
decision. This database is one of the best curated medical eye
databases worldwide.
This two-stage design is beneficial in that when the OCT machine
or image definition changes, only the first part will need to be
retrained. This will help this algorithm become more universally
applicable. In an end-to-end training network, the entire network
would need to be retrained.
DeepMind demonstrated that performance of their AI in making a
referral recommendation, reaches or exceeds that of experts on a
range of sight-threatening retinal diseases. The error rate on
referral decision is 5.5%, exceeding or matching specialists even
when specialists are given fundus images as well as patient notes
in addition to the OCT. Furthermore, the AI beat all retina
specialists and optometrists on selectivity and sensitivity
measures in referring urgent cases. This is clearly the first step,
but an important one that truly opens the door.
Skin disease
Researchers at Heidelberg have already demonstrated that trained
deep neural networks, in this case based on Google's Inception v4
CNN architecture, can recognize melanoma based on dermoscopy
images. These researchers showed that the software achieves 10
percent more specificity than human clinicians when the sensitivity
was set at a level matching human clinicians. The machine can
achieve a high 95% sensitivity at a 63.8% specificity.
This is a promising result that shows such diagnostics can be
automated. Indeed, multiple companies are automating detection of
cancer diseases. One example is SkinVision, from the Netherlands, which seeks to offer a risk
rating of skin cancer based on relatively low-quality smartphone
images. They trained their algorithm on more than 131k images from 31k users in multiple countries. The risk ranking
of the training images were annotated by dermatologists. Studies
show that the algorithm can score a 95.1% sensitivity in detecting
(pre)malignant conditions with 78.3% specificity. These are good
results although the specificity may need to improve as it could
unnecessarily alarm some patients.
The business cases are not just limited to cancer detection.
Haut.AI is an Estonian company that proposes to use images to track
skin dynamics and offer recommendations. One example is that their
AI can be a simple and accurate predictor of chronological age
using just the anonymized images of eye corners. The networks were
trained on 8414 anonymized highâresolution images of eye corners
labelled with the correct chronological age. For people within the
age range of 20 to 80 in a specific population, the machine reaches
a mean absolute error of 2.3 years.
There are naturally many more start-ups active in this field.
Some firms are focused on health diagnostic whilst others are
seeking to use the AI to create tailored skincare regimes and
product recommendation. The path to market, and the regulatory
barriers, for each target function will naturally be
different.
To learn more about this exciting field, please see IDTechEx's
report "Digital Health & Artificial Intelligence 2020: Trends,
Opportunities, and Outlook" by
visiting www.IDTechEx.com/digitalhealth. This report outlines
the state-of-the-art in the use of AI in diagnosing a range of
medical conditions. It also identifies and discusses the progress
of various companies seeking to commercialize such technological
advances. Furthermore, this report considers the trend of digital
health more generally. It provides a detailed overview of the
ecosystem and offers insights into the key trends, opportunities
and outlooks for all aspects of digital health, including:
Telehealth and telemedicine, Remote patient monitoring, Digital
therapeutics / digiceuticals / software as a medical device,
Diabetes management, Consumer genetic testing, Smart home as a
carer and AI in diagnostics.
To connect with others on this topic, register for The IDTechEx
Show! USA 2020, November 18-19 2020, Santa Clara, USA. Presenting the latest emerging
technologies at one event, with six concurrent conferences and a
single exhibition covering 3D Printing and 3D Electronics, Electric
Vehicles, Energy Storage, Graphene & 2D Materials, Healthcare,
Internet of Things, Printed Electronics, Sensors and Wearable
Technology. Please visit www.IDTechEx.com/USA to find out
more.
IDTechEx guides your strategic business decisions through its
Research, Consultancy and Event products, helping you profit from
emerging technologies. For more information on IDTechEx Research
and Consultancy contact research@IDTechEx.com or visit
www.IDTechEx.com.
Media Contact:
Jessica Abineri
Marketing Coordinator
press@IDTechEx.com
+44-(0)-1223-812300
Logo:
https://mma.prnewswire.com/media/478371/IDTechEx_Logo.jpg