Deep Learning Market: Focus on Medical Image Processing, 2020-2030

Overview

Deep learning is a machine learning approach that involves the use of intuitive algorithms and artificial neural networks to facilitate unsupervised pattern recognition/insight generation from large volumes of unstructured data. This technology is gradually being incorporated in a variety of applications across the healthcare sector, including imaging-based medical diagnosis and data processing. Specifically concerning medical imaging, deep learning has the potential to be used to automate information processing and result interpretation for a variety of diagnostic images, such as X-rays, computed tomography scans, magnetic resonance imaging, and positron emission tomography. In this context, it is worth mentioning that the manual examination of medical images is limited, both in terms of accuracy (resulting in misdiagnosis) and throughput (leading to delays in communication of results). As a result, in situations characterized by low physician/pathologist to patient ratios, the conventional mode of operation is rendered inadequate. Experts have predicted a shortage of 10,000 to 40,000 physicians, by 2030, in the US alone. Further, it is estimated that 90% of medical data generated in hospitals is in the form of images; this puts an immense burden on radiologists and other consulting physicians related to processing such large volumes of data. In fact, according to a study published in the American Journal of Medicine, ~15% of reported medical cases in developed countries, are misdiagnosed. In addition, close to 1.5 million individuals are estimated to die each year, across the world, due to misdiagnosis. On the other hand, accurate diagnosis at an early stage has been demonstrated to allow significant cost savings for both patients and healthcare providers. In this scenario, deep learning and other artificial intelligence-based technologies are currently being developed/investigated to automate such processes.

Over time, various industry stakeholders have designed proprietary deep learning algorithms for processing of medical images. Presently, many innovators claim to have developed the means to train computers to read and triage medical images, and recognize patterns related to both temporal and spatial changes (which are not even visible to the naked eye). Experts in this field also believe that the use of deep learning can actually speed up the processing and interpretation of radiology data by 20%, reducing the rate of false positives by approximately 10%. It is also worth mentioning that in the past few years, the FDA has provided the necessary clearances and approved the use for a variety of deep learning software. Moreover, several technology-focused innovators, such as (in alphabetical order) IBM, GE Healthcare and Google, have entered into strategic alliances with big pharma players, in order to bring proprietary deep learning-based medical solutions to the market. This upcoming segment of the pharmaceutical industry that exists at the interface between medicine and information technology, has garnered the attention of prominent venture capital firms and strategic investors. In the long term, the market is anticipated witness significant growth as more machine learning based solutions are approved for use.

Scope of the Report

The ‘Deep Learning Market: Focus on Medical Image Processing, 2020-2030’ report features an extensive study on the current market landscape offering an informed opinion on the likely adoption of such solutions over the next decade. The study presents an in-depth analysis, highlighting the capabilities of various stakeholders engaged in this domain.

In addition to other elements, the report provides

• A detailed review of the current market landscape of deep learning solutions for medical image processing, along with information on their status of development (launched/under development), regulatory approvals (FDA, CE mark, others), type of offering (diagnostic software/tool, diagnostic software/tool + device), type of image processed (X-ray, MRI, CT, ultrasound), application area (lung infections/respiratory disorders, brain injuries/disorders, lung cancer, cardiac conditions/cardiovascular disorders, bone deformities/orthopedic disorders, breast cancer and others). In addition, it presents details of companies developing such solutions, such as their year of establishment, company size, location of headquarters and focus area (in terms of type of deployment model). Further, it highlights key features of each solution and affiliated technologies.
• An in-depth analysis of the contemporary market trends, presented using three schematic representations, including [A] a grid representation illustrating the distribution of solutions based on application area, type of image processed and type of offering and [B] an insightful map representation highlighting the geographical activity of the players.
• Elaborate profiles of key players that are engaged in the development of deep learning-based solutions intended for processing of medical images. Each company profile features a brief overview of the company (including information on year of establishment, number of employees, location of headquarters and key members of the executive team), details of their respective portfolio of solutions, recent developments and an informed future outlook.
• An analysis of the partnerships that have been inked by stakeholders in the domain, during the time period 2016-2020 (till June), covering research/development agreements, solution utilization agreements, solution integration agreements, marketing/distribution agreements, other relevant types of deals.
• An analysis of the investments made, including seed financing, venture capital financing, debt financing, grants and others, in companies that are focused on developing deep learning-based solutions intended for processing of medical images.
• An elaborate valuation analysis of companies that are involved in applying deep learning in solutions intended for processing of medical images. Further, we have built a multi-variable dependent valuation model to estimate the current valuation of a number of companies engaged in this domain.
• A clinical trial analysis of completed, ongoing and planned studies (available on ct.gov), focused on the assessment deep learning-based software solutions, based on various parameters, such as trial registration year, trial recruitment status, trial design, target therapeutic area, leading industry and non-industry players, and geographical locations of trials.
• An in-depth analysis of over 3,000 patents related to deep learning and medical images that have been filed/granted till June 2020, highlighting key trends associated with these patents, across type of patent, publication year and application year, regional applicability, CPC symbols, emerging focus areas, leading patent assignees (in terms of number of patents filed/granted), patent benchmarking and valuation.
• An insightful analysis highlighting cost saving potential associated with the use of deep learning solutions intended for processing of medical images, based on information gathered from close to 30 countries, taking into consideration various parameters, such as total number of radiologists, annual salary of radiologists, number of scans performed (across each type of image) and increase in efficiency by adoption of deep learning solutions.
• An insightful discussion on the views presented by various industry and non-industry experts present across the globe, on various portals, such as YouTube and other media platforms. The summary of insights provided by each expert is discussed across focus area, current industry status/challenges and future outlook.

One of the key objectives of this report was to estimate the existing market size and the future growth potential within the deep learning market (medical image processing segment), such as global radiology spending across countries, number of radiologists employed across different regions of globe, annual salary of radiologists, rate of adoption of deep learning-based solutions, we have developed informed estimates on the financial evolution of the market, over the period 2020-2030. The report also provides details on the likely distribution of the current and forecasted opportunity across [A] application area (lung infections/respiratory disorders, brain injuries/disorders, lung cancer, cardiac conditions/cardiovascular disorders, bone deformities/orthopedic disorders, breast cancer and others), [B] type of image processed (X-ray, MRI, CT, ultrasound) and [C] region (North America, Europe and Asia Pacific/Rest of the World). In order to account for future uncertainties and to add robustness to our forecast model, we have provided three scenarios, namely conservative, base and optimistic scenarios, representing different tracks of the industry’s growth.

The opinions and insights presented in the report were also influenced by discussions held with multiple stakeholders in this domain.

The report features detailed transcripts of interviews held with the following individuals (in alphabetical order):

• Walter de Back (Research Scientist, Context Vision, Q2 2020)
• Dr. Vikas Karade (CEO, AlgoSurg, Q2 2020)
• Babak Rasolzadeh (Senior Director of Product, Arterys, Q2 2020)
• Carla Leibowitz, (Head of Strategy and Marketing, Arterys, Q2 2017)
• Mausumi Acharya, (CEO, Advenio Technosys, Q2 2017)
• Deekshith Marla, (CTO, Arya.ai) and Sanjay Bhadra, (COO, Arya.ai, Q2 2017)

All actual figures have been sourced and analyzed from publicly available information forums. Financial figures mentioned in this report are in USD, unless otherwise specified.

Key Questions Answered

• Who are the leading developers of deep learning-based solutions for medical image processing?
• What are the key application areas for deep learning solutions designed for processing of medical images, such as X-Ray, ultrasound, CT, MRI and others?
• How many solutions based on deep learning technology for processing of medical images have been cleared by FDA or have received CE marking?
• What is the impact of COVID-19 on the demand for deep learning solutions designed for processing of medical images?
• What is the likely valuation/net worth of companies involved in this segment?
• What is the likely cost saving potential associated with the use of deep learning-based solutions for processing of medical images?
• How is the current and future opportunity likely to be distributed across key market segments?
• What is the potential usability of deep learning-based medical image processing solutions for lung scanning in COVID-19 patients?
• Which partnership models are commonly adopted by stakeholders in this industry?
• What is the overall trend of funding and investments in this domain?
• What are the opinions of key opinion leaders involved in the deep learning space?