BOSTON, Oct. 20, 2020 /PRNewswire/ -- Image recognition
AI has the potential to revolutionize medical diagnostics. In
addition to enabling early disease detection and even the
possibility of prevention, it can enhance the workflow of
radiologists by accelerating reading time and automatically
prioritizing urgent cases. However, as IDTechEx has reported
previously in its article "AI in Medical Diagnostics: Current
Status & Opportunities for Improvement", image recognition
AI's current value proposition remains below the expectations of
most radiologists. Over the next decade, AI image recognition
companies serving the medical diagnostics space will need to test
and implement a multitude of features to increase the value of
their technology to stakeholders across the healthcare setting.
This article discusses key technological issues that AI image
diagnostics companies face and provides a roadmap describing when
and how they are likely to be overcome over the next decade. This
article is based on research conducted by IDTechEx in the report
"AI in Medical Diagnostics 2020-2030: Image Recognition, Players,
Clinical Applications, Forecasts". Please refer to the report for
more information.
Combining data sources through sensor fusion
Radiologists have a range of imaging methods at their disposal
and may need to utilize more than one to detect signs of disease.
For example, X-ray and CT scanning are both used to detect
respiratory diseases. X-rays are cheaper and quicker, but CT
scanning provides more detail about lesion pathology due its
ability to form 3D images of the chest. It is sometimes necessary
to follow-up a chest X-ray scan with a CT scan to further
investigate a suspicious lesion, but AI-driven analysis software
can only process one or the other.
To enable efficient analysis of patient scans, image recognition
AI software should be able to combine and interpret data from
different imaging sources to gain a better perspective of the
patient's pathology. This could generate deeper insights into
disease severity and progression, thereby providing radiologists
with a higher level of understanding of the condition of
patients.
Some AI companies are already attempting to train their
algorithms using data gathered from different imaging methods into
one comprehensive analysis, but this remains a challenge for most.
Recognizing signs of disease in images from multiple modalities
requires a level of training far beyond the already colossal
training process for single modality image recognition AI. From a
business perspective, it is currently simply not worth it for
radiology AI companies to explore this due to the sheer quantity of
data sets, time and manpower required to achieve this. This
suggests that sensor fusion will remain an issue for the rest of
the coming decade.
Multiple disease applications
Another important innovation will be to apply image recognition
AI algorithms to multiple diseases. Currently, many AI-driven
analysis tools can only detect a restricted range of pathologies.
Their value in radiology practices is hence limited as the
algorithms may overlook or misconstrue signs of disease that they
are not trained on, which could lead to misdiagnosis. Such issues
could lead to a mistrust of AI tools by radiologists, which may in
turn reduce their rate of implementation in medical settings.
In the future, AI algorithms will recognize not just one but
various conditions from a single image or data set (e.g., multiple
retinal diseases from a single fundus image). This is already a
reality for numerous radiology AI companies. For example,
DeepMind's and Pr3vent's solutions are designed to detect over 50
ocular diseases from a single retinal image, while VUNO's
algorithms can detect a total of 12.
Detecting multiple pathologies from the same images requires
expert radiologists to provide detailed annotations of each
possible abnormality in a photo, and to repeat this process
thousands or even millions of times, which is highly time-consuming
and thus expensive. As a result, some companies prefer to focus on
a single disease. Allocating the resources to achieving multiple
disease detection capabilities will be worth it on the long run for
AI companies, however. Software capable of detecting multiple
pathologies offer much greater value than those built to detect a
specific pathology as they are more reliable and have wider
applicability. Companies offering single-disease application
software will soon be forced to extend their product's application
range to stay afloat in this competitive market.
All-encompassing training data
A key technical and business advantage lies in the demonstration
of success in dealing with a wide range of patient demographics as
it widens the software's applicability. AI software must work
equally well for males and females, different ethnicities, etc.
While training DL algorithms to detect a specific disease, the
training data should encompass numerous types of abnormalities
associated with this disease. This way, the algorithm can recognize
signs of the disease in a multitude of demographics, tissue types,
etc. and achieve the level of sensitivity and specificity required
by radiologists. For instance, breast cancer detection algorithms
need to recognize lesions in all types of breast (e.g. different
densities). Another example is skin cancer. Historically, skin
cancer detection algorithms have struggled to distinguish
suspicious moles in dark skin tones as changes in the appearance of
the moles are more challenging to identify. These algorithms must
be able to examine moles in all skin types and colors. From an
image of a suspicious mole, the software should also be able to
recognize the stage of disease progression based on its shape,
color and diameter. Otherwise, if an algorithm encounters a type of
abnormality that doesn't match any of the conditions it recognizes,
it will classify it as "not dangerous" as it doesn't associate it
with any condition that it knows. Having a diverse data set also
helps to prevent bias (the tendency of an algorithm to make a
decision by ignoring options that go against its initial
assessment).
Reduced neural network complexity
The architecture of AI models used in medical image analysis
today tends to be convoluted, which extends the development process
and increases the computing power required to utilize the software.
Companies developing the software must ensure that their computing
power is sufficient to support customers' activities on their
servers, which requires the installation of expensive Graphical
Processing Units (GPUs). In the future, reducing the number of
layers while maintaining or improving algorithm performance will
represent a key milestone in the evolution of image recognition AI
technology. It would decrease the computing power required,
accelerate the results generation time due to shorter processing
pathways and ultimately reduce server costs for AI companies.
Imaging equipment neutrality
The installation of AI software for medical image analysis can
sometimes represent a significant change to hospitals' and
radiologists' workflow. Although many medical centres welcome the
idea of receiving decision support through AI, the reality of going
through the installation process can be daunting enough to deter
certain hospitals.
As a result, software providers put a lot of effort into making
their software universally compatible so that it fits directly into
radiologists' setups and workflows. This will become an
increasingly desirable feature of image recognition AI as customers
favor software that is compatible with all major vendors, brands
and models of imaging equipment. This is, broadly speaking, already
a reality as most FDA-cleared algorithms are vendor neutral,
meaning that they can be applied to most types of scanner brands
and models.
Access to patient information and recommended treatment
plan
Today, AI algorithms only have access to medical imaging data.
As such, the condition and medical history of patients are unknown
to the AI software during the analytical process. Because of this
limitation, the software is restricted to locating abnormalities,
providing quantitative information and, in some cases, assessing
the risk of disease.
While these insights can be highly valuable to doctors,
particularly when done faster and more accurately than human
doctors, AI can do more. To utilize the full capabilities of AI and
provide additional value in medical settings, software developers
must focus on post-diagnosis support too. Although this remains a
rare aspect of medical image recognition AI as of mid-2020,
companies are starting to explore this possibility.
Some skin cancer detection apps such as MetaOptima and
SkinVision provide actionable recommendations for further action
after an assessment is made. These include scheduling subsequent
appointments for follow-up or biopsy or setting reminders next skin
checks. Post-diagnosis support is becoming a desirable feature as
it complements the doctor's evaluation, almost like a second
opinion, and thus provides the doctor with more confidence in their
assessment. To learn more, please refer to IDTechEx's report "AI in
Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical
Applications, Forecasts".
Ultimately, doctors seek a solution that aids them to establish
viable treatment strategies. To achieve this, the software needs
information relating to patients' electronic health records,
clinical trial results, drug databases and more. This goes beyond
simple image recognition. Most companies currently have no
confirmed plans to address this. Implementation of these systems
will remain a work in progress for the next decade and beyond due
to technical challenges caused by the overlap and interoperability
required between various hospital and external databases.
Equipment-integrated AI software
The idea of integrating image recognition AI software directly
into imaging equipment (e.g. MRI or CT scanners) is gaining
momentum as it would facilitate the automation of medical image
analysis. In addition, it avoids problems with connectivity as no
cloud access is required. This is being done more and more - recent
examples include Lunit's INSIGHT CXR software integration into GE
Healthcare's Thoracic Care Suite and MaxQ AI's Intracranial
Haemorrhage (ICH) technology being embedded into Philips' Computed
Tomography Systems.
A downside of integrating AI software into imaging equipment is
that the hospital/radiologist has no flexibility to choose the
provider/software that best suits their needs. The value of this
approach depends on the performance level and capabilities of the
integrated AI software, and if it matches the user's requirements.
If that is not the case, hospitals are likely to favor cloud-based
software.
From the equipment manufacturer's point of view, the business
advantage of integrating image recognition AI into their machines
is clear. The enhanced analytical capabilities provided by the AI
software would give OEM manufacturers a competitive edge as they
render the machines more appealing to hospitals seeking to boost
revenues by maximizing the number of patients seen every day.
From a software provider's perspective, the situation is less
clear. AI radiology companies are currently considering the
advantages of entering exclusive partnerships with manufacturers
versus making their software available as a cloud-based service.
IDTechEx expects a divide to arise among AI radiology companies in
the next 5-10 years. Some will choose the safe option of selling
their software exclusively to large imaging equipment vendors due
to the security that long-term contracts can provide. Others will
lean more towards continuing with the current business model of
catering directly to radiology practices.
IDTechEx's report "AI in Medical Diagnostics 2020-2030: Image
Recognition, Players, Clinical Applications, Forecasts" provides a
detailed analysis of emerging solutions and innovations in the
medical image recognition AI space. It cuts through the
technological landscape from both a commercial and technical
perspective by benchmarking the products of over 60 companies
across 12 disease applications according to performance, market
readiness, technical maturity, value proposition and other factors.
In-depth insights into the company and market landscape are also
provided, including ten-year forecasts for each application.
For more information on this AI in Medical Diagnostics market
report, please visit [i]www.IDTechEx.com/AIMed or for the full
portfolio of Artificial Intelligence research available from
IDTechEx please visit www.IDTechEx.com/Research/AI.
 IDTechEx guides your strategic business decisions through
its Research, Consultancy and Subscription 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:
Natalie Moreton
Digital Marketing Manager
press@IDTechEx.com
+44(0)1223 812300
Image Download:
https://www.dropbox.com/sh/us06zs01ulhw5cu/AADR78nc1yQNY8q5H8JnSS7ra?dl=0
Social Media Links:
Twitter: https://www.twitter.com/IDTechEx
LinkedIn: https://www.linkedin.com/company/idtechex/
Facebook: https://www.facebook.com/IDTechExResearch
Photo:
https://mma.prnewswire.com/media/1315553/IDTechEX_Technological_Roadmap_IRAI.jpg
Logo:
https://mma.prnewswire.com/media/478371/IDTechEx_Logo.jpg