News | Artificial Intelligence | February 15, 2017

Deep Learning in Medical Imaging to Create $300 Million Market by 2021

While early iterations have been met with skepticism, many radiologists are taking a wait-and-see approach

deep learning, artificial inteligence, medical imaging, Signify Research market report, 2021
Signify Research, medical imaging, deep learning, image analysis
Signify Research, world market, medical image analysis, deep learning, artificial intelligence

February 15, 2017 — Deep learning, also known as artificial intelligence, will increasingly be used in the interpretation of medical images to address many long-standing industry challenges. This will lead to a $300 million market by 2021, according to a new report by Signify Research, an independent supplier of market intelligence and consultancy to the global healthcare information technology industry.

In most countries, there are not enough radiologists to meet the ever-increasing demand for medical imaging. Consequently, many radiologists are working at full capacity. The situation will likely get worse, as imaging volumes are increasing at a faster rate than new radiologists entering the field. Even when radiology departments are well-resourced, radiologists are under increasing pressure due to declining reimbursement rates and the transition from volume-based to value-based care delivery. Moreover, the manual interpretation of medical images by radiologists is subjective, often based on a combination of experience and intuition, which can lead to clinical errors.

A new breed of image analysis software that uses advanced machine learning methods, e.g. deep learning, is tackling these problems by taking on many of the repetitive and time-consuming tasks performed by radiologists. There is a growing array of “intelligent” image analysis products that automate various stages of the imaging diagnosis workflow. In cancer screening, computer-aided detection can alert radiologists to suspicious lesions. In the follow-up diagnosis, quantitative imaging tools provide automated measurements of anatomical features. At the top-end of the scale of diagnostic support, computer-aided diagnosis provides probability-driven, differential diagnosis options for physicians to consider as they formulate their diagnostic and treatment decisions.

“Radiology is evolving from a largely descriptive field to a more quantitative discipline. Intelligent software tools that combine quantitative imaging and clinical workflow features will not only enhance radiologist productivity, but also improve diagnostic accuracy,” said Simon Harris, principal analyst at Signify Research and author of the report.

However, it is early days for deep learning in medical imaging. There are only a handful of commercial products and it is uncertain how well deep learning will cope with variations in patient demographics, imaging protocols, image artifacts, etc. Many radiologists were left underwhelmed by early-generation computer-aided detection, which used traditional machine learning and relied heavily on feature engineering. They remain skeptical of machine learning’s abilities, despite the leap in performance of today’s deep learning solutions, which automatically learn about image features from radiologist-annotated images and a "ground-truth”. Furthermore, the “black box” nature of deep learning and the lack of traceability as to how results are obtained could lead to legal implications. While none of these problems are insurmountable, healthcare providers are likely to take a ‘wait and see’ approach before investing in deep learning-based solutions.

“Deep learning is a truly transformative technology and the longer-term impact on the radiology market should not be underestimated. It’s more a question of when, not if, machine learning will be routinely used in imaging diagnosis”, Harris concluded.

“Machine Learning in Medical Imaging – 2017 Edition” provides a data-centric and global outlook on the current and projected uptake of machine learning in medical imaging. The report blends primary data collected from in-depth interviews with healthcare professionals and technology vendors, to provide a balanced and objective view of the market.

For more information: www.signifyresearch.net

Related Content

Cianna Medical featured its wire-free marker system on the exhibit floor of the breast imaging symposium in Hollywood, Fla.

Cianna Medical featured its wire-free marker system on the exhibit floor of the breast imaging symposium in Hollywood, Fla.

Feature | Breast Imaging | April 24, 2019 | By Greg Freiherr
Wires have traditionally been placed prior to lumpectomy to mark cancerous tissues in the breast.
Graphic courtesy of Pixabay

Graphic courtesy of Pixabay

Feature | Artificial Intelligence | April 22, 2019 | By Greg Freiherr
...
HHS Extends Comment Period for Proposed Electronic Health Information Interoperability Rules
News | Electronic Medical Records (EMR) | April 19, 2019
The U.S. Department of Health and Human Services (HHS) is extending the public comment period by 30 days for two...
FDA Clears GE's Deep Learning Image Reconstruction Engine
Technology | Computed Tomography (CT) | April 19, 2019
GE Healthcare has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) of its Deep Learning Image...
In a demonstration on the exhibit floor of the SBI symposium, Koios software identified suspicious lesions in ultrasound images

In a demonstration on the exhibit floor of the SBI symposium, Koios software identified suspicious lesions in ultrasound images. Photo by Greg Freiherr

Feature | Artificial Intelligence | April 19, 2019 | By Greg Freiherr
Commercial efforts to develop...
Artificial Intelligence Performs As Well As Experienced Radiologists in Detecting Prostate Cancer
News | Artificial Intelligence | April 18, 2019
University of California Los Angeles (UCLA) researchers have developed a new artificial intelligence (AI) system to...
Atrium Health Debuts Amazon Alexa Skill to Help Patients Access Medical Care
News | Artificial Intelligence | April 18, 2019
Atrium Health patients will now be able to use Amazon’s electronic voice system Alexa to not only locate the nearest...
Oxipit Introduces Multilingual Support for ChestEye AI Imaging Suite
News | Artificial Intelligence | April 16, 2019
The CE-certified ChestEye artificial intelligence (AI) imaging suite by Oxipit is now available in seven European...