의료 진단분야의 AI (2020-2030년): 이미지 인식, 기업, 임상 응용, 예측: IDTechEx

의료 진단분야의 이미지 인식 AI 기술은 2030년까지 30억 달러 이상의 가치를 가질 것이다

의료 진단분야의 AI (2020-2030년): 이미지 인식, 기업, 임상 응용, 예측

이미지 인식 AI 성능, 시장 준비성, 가치 제안, 기술 성숙도 등에 대해, 60개 이상의 기업을 벤치마킹한다. 12개의 임상 응용에 대한 세부적인 예측, 처리 가능한 시장 규모 및 2030년까지의 침투에 대한 통찰력.


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이 보고서는 5가지 주요 임상 응용을 분류하여, 질병 감지를 위한 이미지 인식 A) 분야를 탐구한다. 보고서에서는, 신흥 기술을 설명하고 주요 업체를 강조하며 각 부문에 대한 시장 환경을 분석한다. 추세, 기회, 위협 및 과제에 대해 (기술 및 상업 모두) 자세히 논의하여, 이 기술의 현재 및 미래 시장에 대한 자세한 통찰력을 제공한다.
Between 2010-2014, the field of image recognition and analysis was revolutionised by the introduction of deep learning, which enabled unprecedented performance leaps. These rapid advancements are fuelling the development of automated, accurate, accessible, and cost-effective medical diagnostics. Since 2010, over 60 entities including 40 new firms globally have set out to capitalise on these technological advances, seeking to commercialise AI-based diagnostics services in fields such as cancer and cardiovascular disease (CVD). More than $2.2 billion has been invested in new start-ups, with the investment since 2017 being 200% higher than the total since 2010.
 
IDTechEx expects the market for AI-enabled image-based medical diagnostics to grow by nearly 10,000% until 2040 whilst the global addressable market (scan volume regardless of processing method) will grow by 50%. In value terms, we forecast the market for AI-enabled image-based medical diagnostics to exceed $3 billion by 2030 across five segments including cancer and CVD as well as respiratory, retinal and neurodegenerative diseases.
 
Technical threshold for automation reached, but is it a point of differentiation?
Algorithms are faster than humans and can be implemented on a massive scale with access to sufficient computing power via the cloud or even at the edge. Until recently, traditional hand-crafted algorithms would fail to meet the fundamental technical pre-requisite that they match or exceed the performance of human experts. The chart below shows that this is no longer the case and that this minimum technical milestone has already been surpassed, clearing away important technical barriers that had long held back automation in this field.
 
The report constructs realistic roadmaps, quantitatively outlining the current status, showing what challenges the technology still faces, and discussing how it is likely to evolve. The report identifies key commercial and technological issues currently limiting the uptake of image recognition AI and provides roadmaps describing when and how they are likely to be overcome over the next decade.
 
 
The chart above shows the sensitivity and specificity of over 35 algorithms implemented on various disease types. The shaded areas represent the range of human performance. The data was compiled by IDTechEx Research.
Source: IDTechEx report "AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts"
Rapid rise in company formation and investment
Since leaps in AI-based image recognition technology opened the market gates, over 60 entities including 40 new firms globally have set out to commercialize AI-based medical diagnostics based on various imaging modalities. The inset below shows the trend in company formation. Interestingly, this trend clearly correlates with the annual improvement reported in image recognition error rates between 2010 and now, highlighting how the technical and commercial developments are fully intertwined.
 
The chart also shows that money has been flowing into the start-up scene. Interestingly, the invested amounts have risen in the immediate past, partly reflecting the fact that the post-tech-demonstration companies now need larger financial reservoirs to pursue a scale-up strategy and to survive a potential consolidation phase. The inset also shows the more popular focus areas. In short, the disease segments with high scanned volumes and/or high value (e.g., preventing mortality with early detection) have received the most money thus far.
 
 
Investments into image recognition AI companies by year 2010-2019. Shades of blue in the chart refer to different companies.
Inset left: Cumulative number of active image recognition AI companies founded since 2010, displayed as a normalised change with relation to 2010.
Inset right: Investments into image recognition AI companies by disease application since 2010.
Source: IDTechEx report "AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts"
Company landscape and benchmarking
Each company, or each product of a company, sits at a different stage of readiness. The chart below provides a snapshot for commercialisation stage in cancer detection. Although multiple firms are already selling, this alone does not guarantee success. Companies are trying one or multiple of the following approaches to succeed:
 
  • Towards wider applicability: The days of leaps in performance of image recognition are over, barring radical innovation in algorithm techniques. The gains in precision, recall and other metrics will henceforth be incremental. As such, the emphasis has shifted to other points. Of importance is showing that the AI is applicable to as wide a population set (ie: gender, age, ethnicity, tissue density, etc.) as possible.
  • Evolving beyond simple abnormality identification towards super-human insights: Whilst there is a spread in what different algorithms are offering, most are positioned as decision support tools. The next evolution will be to provide further information and explanations alongside the detection and segmentation. Some are even aiming to suggest treatment options, hoping to evolve beyond the radiologist scope and to encroach into the doctors' sphere of competency, although this is generally further down the line. In short, the goal is to raise the AI complexity beyond anomaly detection.
  • Scale: Our view is that scale will matter in this business. Large scale, if done right, (a) means more access to data, which translates into an ever widening performance gap against competitors in terms of algorithm accuracy, versatility, and applicability; (b) creates a one-stop-shop proposition, helping with the sales and customer acquisition process; (c) results in larger technical teams that can aid the on-site into-work-flow integration process, which in turn boosts installed base and acts a lock-in mechanism. In general, scale can help the winners drive consolidation.
 
The report analyses the competitive landscape, identifying and reviewing more than 60 companies and leading research institutes. It identifies key players in each sector and provides detailed insights into the market readiness of companies' AI software for a variety of disease applications. To facilitate the differentiation of each company, they are segmented and categorised by disease application, geography, modality, state of product development and more. IDTechEx also reveals the level of investments generated by image recognition AI technology and highlights the funding secured by individual companies and applications of this field.
 
The report contains over 50 company profiles covering large computing corporations, academic institutions, research centres and disruptive start-ups. IDTechEx analysts go far beyond what is publicly available by conducting an extensive number of primary interviews, providing the latest and most important information to the reader.
 
We have developed parameters to benchmark the strength of the companies' technology, including performance (e.g.: accuracy, sensitivity, specificity), state of development, market readiness, software complexity, value add (ie: depth of insights provided to doctors), technical maturity and more. We also discuss other non-technical aspects vital to the success of companies beyond a good tech story.
 
 
Cancer detection AI companies: State of product development. The report assesses the commercial status and state of development of over 60 companies and their products. It also assesses the technical maturity and value proposition of each company's software. In addition, the report details sector-specific roadblocks in the path to market. Note: Although large computing corporations such as Google are still developing their product, their CNN architectures are widely used by companies on the market (eg: Google's Inception).
Source: IDTechEx report "AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts"
Current and future market trends
AI in medical image diagnostics is already in existence and numerous companies are past clinical stages in many segments. Our assessment is that the inflection point is near and likely to be reached around 2023-2025.
 
Indeed, we have developed short- and long-term market forecasts from 2020 to 2040. We have estimated and forecasted the total addressable market per disease type in scan volume. We have leveraged out technological understanding and market insight to develop realistic market penetration and cost evolution projections per disease type, allowing us to create segmented market forecasts in scan volume and market value. The report highlights the current and future market share and size for 12 different applications and markets, including the detection of five types of cancer and four aspects of CVD. Insights into pricing strategies, uptake-limiting roadblocks and commercial opportunities complement our analysis to provide a comprehensive perspective of the market today and in the future.
 
 
Market forecast 2020-2031 by disease application.
Inset: Scan volume forecast 2020-2040. Bar charts show the addressable market in terms of scan volumes. Continuous line is the average of AI-enabled scan volumes weighted across different disease areas. To learn the dynamics of individual market segments please consult the report.
Source: IDTechEx report "AI in Medical Diagnostics 2020-2030: Image Recognition, Players, Clinical Applications, Forecasts"
 
Key questions answered in this report:
 
  • How does AI performance in image analysis compare to human level in accuracy, speed, and cost?
  • What does the competitive landscape look like in each disease segment? Who are the key players and emerging start-ups? How do companies benchmark and differentiate? How can they grow beyond a good tech story?
  • How do competitors' AI software compare to each other and which companies are leading the pack?
  • How is the AI software distributed? Can it be integrated into imaging equipment?
  • What is the state of development of this technology today and how will it evolve in the next ten years in performance and beyond simple anomaly detection tasks?
  • What are the main clinical applications of image recognition AI? What are the drivers and constraints of market growth in each segment?
  • How can pricing strategies impact the implementation of image recognition AI in medical diagnostics?
  • What is the market (volume and value) of this technology, and how and why will this change over the next decade?
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Table of Contents
1.EXECUTIVE SUMMARY
1.1.Report scope
1.2.Image recognition AI in medical imaging
1.3.Drivers & constraints of AI in medical imaging
1.4.Benefits of using AI in medical diagnostics
1.5.Clinical applications of image recognition AI covered in this report
1.6.Investments into image recognition AI companies by disease application
1.7.Image recognition AI: Performance comparison by application
1.8.Cancer detection AI: State of development and market readiness
1.9.Cancer detection AI companies: State of product development
1.10.Cancer detection AI: Conclusions and outlook
1.11.Cancer detection AI: Conclusions and outlook (2)
1.12.CVD detection AI: State of development and market readiness
1.13.CVD detection AI companies: State of product development
1.14.CVD detection AI: Conclusions and outlook
1.15.Respiratory diseases detection AI: State of development and market readiness
1.16.Respiratory diseases detection AI companies: State of product development
1.17.Respiratory diseases detection AI: Conclusions and outlook
1.18.Retinal diseases detection AI: State of development and market readiness
1.19.Retinal diseases detection AI companies: State of product development
1.20.Retinal diseases detection AI: Conclusions and outlook
1.21.Retinal diseases detection AI: Conclusions and outlook (2)
1.22.NDD detection AI: State of development and market readiness
1.23.NDD detection AI companies: State of product development
1.24.NDD detection AI: Conclusions and outlook
1.25.Image recognition AI: Technological roadmap
1.26.Image recognition AI: Roadmap of factors limiting penetration
1.27.Image recognition AI: Market penetration 2020-2040
1.28.Market forecast 2020-2031 by disease application
1.29.AI provides real value and the market is rapidly growing
1.30.Remaining challenges
1.31.Opportunities for technological improvements
1.32.Why do image recognition AI companies struggle to achieve profitability?
2.INTRODUCTION
2.1.Report scope
2.2.Medical imaging advances diagnostics
2.3.Types of medical imaging
2.4.Uses, pros and cons of each type of imaging
2.5.X-radiation (X-ray)
2.6.Computed tomography (CT)
2.7.Positron emission tomography (PET)
2.8.Magnetic resonance imaging (MRI)
2.9.Ultrasound
2.10.Imaging devices: Regulations & path to approval
2.11.Radiation from imaging devices: Safety regulations
2.12.Image recognition AI in medical imaging
2.13.Drivers & constraints of AI in medical imaging
2.14.AI in healthcare: Existing regulations
2.15.AI in healthcare: Regulations & path to approval
2.16.Clinical applications of image recognition AI covered in this report
2.17.Interest in AI and deep learning has soared in the last five years...
2.18.... And so have investments into image recognition AI companies
2.19.CVD and cancer have generated the most funding
3.ARTIFICIAL INTELLIGENCE, DEEP LEARNING AND CONVOLUTIONAL NEURAL NETWORKS
3.1.What is artificial intelligence (AI)? Terminologies explained
3.2.The two main types of AI in healthcare
3.3.Requirements for AI in medical imaging
3.4.Main deep learning (DL) approaches
3.5.DL makes automated image recognition possible
3.6.Image recognition AI is based on convolutional neural networks (CNNs)
3.7.Workings of CNNs: How are images processed?
3.8.Workings of CNNs: Another example
3.9.Common CNN architectures for image recognition
3.10.Milestones in DL: Image recognition surpasses human level
3.11.How do image recognition AI algorithms learn to detect disease?
3.12.The depth and variation of training data dictate the robustness of image recognition AI algorithms
3.13.Assessing algorithm performance: The importance of true/false positives/negatives
3.14.Measures in deep learning: Sensitivity and Specificity
3.15.DL algorithms assess the rate of true/false positives/negatives to determine sensitivity and specificity
3.16.Measures in deep learning: Area Under Curve (AUC) or area under curve of receiver operating characteristics (AUCROC)
3.17.When AUC is not a good measure of the algorithm success?
3.18.Measures in deep learning: Reproducibility
3.19.F1 Score
3.20.Benefits of using AI in medical diagnostics
3.21.Drivers of image recognition AI usage
3.22.Limiting factors of image recognition AI using CNNs
4.CANCER
4.1.Image recognition enhances cancer diagnostic solutions
4.2.Investments into cancer detection AI companies
4.3.Image recognition AI for cancer detection: Key players
4.4.Breast cancer
4.5.Breast cancer: Detection and quantification of breast densities via mammography (2018)
4.6.Breast cancer screening via mammograms and pathology slides
4.7.Lunit: Breast cancer screening via mammography
4.8.Reproducible breast cancer screening: Densitas, Kheiron Medical, and Therapixel
4.9.Therapixel: Early breast cancer detection
4.10.CureMetrix: AI estimates the risk of disease
4.11.Google: Surpassing human performance regardless of patient population type
4.12.Google: Surpassing human performance regardless of patient population type (2)
4.13.On the market: Intrasense, ScreenPoint Medical, Qlarity Imaging and Koios Medical
4.14.Currently on the market or upcoming: Qview Medical, PathAI and Zebra Medical Vision
4.15.AI performance comparison: Methodology
4.16.Breast cancer detection AI: Performance comparison
4.17.Breast cancer detection AI: Performance comparison (2)
4.18.Lung cancer
4.19.Lung cancer: NYU uses DL on lung cancer histopathological images to identify cancer cells, determine their type, and predict what somatic mutations are present in the tumour
4.20.Lung cancer: Detection made easier
4.21.Infervision: Detecting nodules three times faster than radiologists
4.22.Enlitic: Identifying malignant lung nodules 18 months sooner
4.23.Arterys: Accelerating reading time by 45%
4.24.Additional players: VUNO, Lunit, Intrasense & VoxelCloud
4.25.Additional players: Behold.ai, Aidence, Mindshare Medical & Riverain Technologies
4.26.Lung cancer detection AI: Performance comparison
4.27.Lung cancer detection AI: Performance comparison (2)
4.28.Skin cancer
4.29.Skin cancer: Key players
4.30.Skin cancer: Machine learning algorithms
4.31.Skin cancer: The ABCDE criteria
4.32.Skin cancer: Dermoscopic melanoma recognition (2018)
4.33.Skin cancer: Dermoscopic melanoma recognition and its challenges
4.34.Miiskin: Tracking skin changes over time
4.35.SkinVision: Risk assessment and unparalleled accuracy at a low cost
4.36.MetaOptima: Medical grade image quality for the consumer
4.37.Stanford University: Automated classification of skin lesions
4.38.Mole mapping apps track skin changes over time: SkinIO, Skin Analytics & University of Michigan
4.39.Skin cancer detection AI: Performance comparison
4.40.Skin cancer detection AI: Performance comparison (2)
4.41.Thyroid cancer: AmCad BioMed automatically identifies nodules
4.42.Prostate cancer: Cortechs Labs improves a key visualisation and quantification method
4.43.Prostate cancer: Intrasense and YITU Technology
4.44.Microsoft: Using AI for cancer detection, radiotherapy planning and outcome monitoring
4.45.AI-driven histological analysis of tissue slides for cancer detection: Paige & Primaa
4.46.Cancer detection AI: Performance comparison
4.47.Cancer detection AI: State of development and market readiness
4.48.Cancer detection AI applications: State of development
4.49.Cancer detection AI companies: State of product development
4.50.Cancer detection AI companies: Software complexity
4.51.Conclusions and outlook
4.52.Conclusions and outlook (2)
5.CARDIOVASCULAR DISEASE
5.1.What is cardiovascular disease (CVD) and where does image recognition AI apply?
5.2.AI can provide solutions to improve CVD management
5.3.Investments into CVD detection AI companies
5.4.Using imaging & AI to detect clots and blockages
5.5.Key players
5.6.Stroke
5.7.Stroke detection AI: Key players
5.8.MIT: A DL solution for stroke detection from CT scans
5.9.iSchemaView: Categorising the extent and location of ischemic injury up to 30 hours post-symptoms onset
5.10.Infervision: Dynamic and risk assessment of active bleeding
5.11.MaxQ AI: Near real-time detection, triage and annotation of stroke injury
5.12.Qure.ai: Identifying 5 types of intracranial haemorrhages
5.13.Other stroke detection companies: Aidoc, Zebra Medical Vision and Quantib
5.14.Stroke detection AI: Performance comparison
5.15.Stroke detection AI: Performance comparison (2)
5.16.Coronary heart disease (CHD) & myocardial infarction
5.17.CHD detection AI: Key players
5.18.CHD: Cornell & NYU's DL approach to diagnosis
5.19.HeartFlow: Assessing the impact of coronary blockages on cardiac blood supply
5.20.Circle Cardiovascular Imaging: Automated plaque assessment
5.21.Other CHD detection AI companies: Intrasense, CASIS & VoxelCloud
5.22.Assessing blood flow
5.23.Assessing blood flow: Key players
5.24.Arterys: Quantifying blood flow in minutes
5.25.Pie Medical Imaging: Calculating blood flow from 3D phase-contrast MR images
5.26.On the market: NeoSoft, HeartFlow, iSchemaView & Circle Cardiovascular Imaging
5.27.Blood flow detection AI: Performance comparison
5.28.Cardiac function
5.29.Ejection fraction is key for evaluating cardiac function, and AI allows for more accurate measurements
5.30.Cardiac function detection AI: Key players
5.31.Philips: Assessing cardiac performance, strength and structure
5.32.NeoSoft: automated segmentation for cardiac function and myocardial characterisation
5.33.Other cardiac function players: TomTec, DiA Imaging Analysis, GE Healthcare & BioMedical Image Analysis Group
5.34.Cardiac function detection AI: Performance comparison
5.35.CVD detection AI: Performance comparison
5.36.CVD detection AI: State of development and market readiness
5.37.CVD detection AI applications: State of development
5.38.CVD detection AI companies: State of product development
5.39.CVD detection AI companies: Software complexity
5.40.Conclusions and outlook
6.RESPIRATORY DISEASES
6.1.How can AI improve respiratory disease diagnosis?
6.2.Investments into respiratory diseases detection AI companies
6.3.Key players
6.4.VIDA: Identifying asthma and COPD
6.5.Infervision: Level of pneumonia infection as a percentage
6.6.SemanticMD: Probability score for tuberculosis
6.7.Lunit: Algorithm detects 9 different respiratory disorders
6.8.VUNO: Cutting image reading time by half
6.9.Arterys: Displaying negative findings for rule out support
6.10.Qure.ai: Detecting multiple chest abnormalities
6.11.AI embedded into imaging device: GE Healthcare
6.12.On the market: Aidoc, Zebra Medical Vision, Intrasense & Behold.ai
6.13.In development: Artelus, Enlitic & SigTuple
6.14.Respiratory diseases detection AI: Performance comparison
6.15.Respiratory diseases detection AI: Algorithm comparison (2)
6.16.COVID-19
6.17.COVID-19: Key players
6.18.COVID-19: Infervision
6.19.COVID-19: Other companies
6.20.COVID-19 detection AI: Performance comparison
6.21.COVID-19 detection AI: Algorithm comparison (2)
6.22.Respiratory diseases detection AI: Performance comparison
6.23.Respiratory diseases detection AI: State of development and market readiness
6.24.Respiratory diseases detection AI applications: State of development
6.25.Respiratory diseases detection AI companies: State of product development
6.26.Respiratory diseases detection AI companies: Software complexity
6.27.Conclusions and outlook
7.RETINAL DISEASES
7.1.What are retinal diseases and how are they detected?
7.2.AI can reach expert level of disease detection in 10 days, compared to 20 years for humans
7.3.Investments into retinal diseases detection AI companies
7.4.Key players
7.5.Artelus: Detecting DR by ensuring image quality
7.6.VUNO: Identifying 12 types of eye disorders
7.7.SemanticMD: AI solution for use offline
7.8.SigTuple: Applying AI to multiple imaging modalities
7.9.Pr3vent: Detects 50+ pathologies in newborns
7.10.Currently in clinical trials: Novai, Verily & Capital University of Medical Sciences
7.11.On the market or upcoming: VoxelCloud, Singapore National Eye Centre & CERA
7.12.Retinal diseases detection AI: Performance comparison
7.13.Retinal diseases detection AI: Performance comparison (2)
7.14.Retinal diseases detection AI: Performance comparison (3)
7.15.Retinal diseases detection AI: State of development and market readiness
7.16.Retinal diseases detection AI companies: State of product development
7.17.Retinal diseases detection AI companies: Software complexity
7.18.Conclusions and outlook
7.19.Conclusions and outlook (2)
8.NEURODEGENERATIVE DISEASES
8.1.AI can identify signs of dementia years before its onset
8.2.Investments into neurodegenerative diseases detection AI companies
8.3.Key players
8.4.Quantib: Measuring brain size and atrophy
8.5.Icometrix: Diagnosing various NDDs
8.6.Cortechs Labs: Automated quantification of brain structure volume
8.7.Avalon AI: Interpreting multiple MRI modalities
8.8.VUNO: Immediate segmentation and parcellation
8.9.University of Bari: Predicting Alzheimer's disease up to a decade before onset
8.10.On the market: Qure.ai & Siemens Healthineers
8.11.In development: IDx, Icahn School of Medicine, UCSF
8.12.Research only: BioMedical Image Group, Imperial College London & University of Edinburgh
8.13.Research only: McGill & University College London
8.14.NDD detection AI: Performance comparison
8.15.NDD detection AI: Performance comparison (2)
8.16.NDD detection AI: Performance comparison (3)
8.17.NDD detection AI: State of development and market readiness
8.18.NDD detection AI companies: State of product development
8.19.NDD detection AI companies: Software complexity
8.20.Conclusions and outlook
9.MARKET ANALYSIS
9.1.Geographic segmentation: Almost 50% of medical diagnostics AI companies are based in the USA
9.2.Modality segmentation: Over half of medical diagnostics AI companies focus on CT and X-ray imaging
9.3.Image recognition AI: Technological roadmap
9.4.Image recognition AI: Technological roadmap (2)
9.5.Image recognition AI: Technological roadmap (3)
9.6.Image recognition AI: Roadmap of factors limiting penetration
9.7.Image recognition AI: Roadmap of factors limiting penetration (2)
9.8.Image recognition AI: Roadmap of factors limiting penetration (3)
9.9.Market analysis methodology
9.10.Addressable markets are growing, with some exceptions, and AI use is expected to mirror this trend
9.11.Scan volume per year: AI use will rise as its adoption increases
9.12.Image recognition AI: Market penetration 2020-2040
9.13.Image recognition AI: Market penetration 2020-2040 (2)
9.14.Image recognition AI: Market penetration 2020-2040 (3)
9.15.Image recognition AI: Market penetration 2020-2040 (4)
9.16.Business models: Subscription vs Pay Per Use
9.17.Market share in 2019: CVD detection
9.18.Market forecast 2020-2031 by disease application
9.19.Market forecast 2020-2031: CVD detection
9.20.Market forecast 2020-2031: Cancer detection
10.CONCLUSIONS & OUTLOOK
10.1.AI provides real value and the market is rapidly growing
10.2.Remaining challenges: Improving data curation and algorithm training procedures
10.3.Remaining challenges: Need for clearer images
10.4.Remaining challenges: Regulations and data privacy
10.5.Opportunities for technological improvements
10.6.Cloud-based vs offline software
10.7.Moving towards equipment-integrated AI software?
10.8.Why do image recognition AI companies struggle to achieve profitability?
11.LIST OF COMPANIES
11.1.Cancer detection AI
11.2.CVD detection AI
11.3.Respiratory diseases detection AI
11.4.Retinal diseases detection AI
11.5.Neurodegenerative diseases detection AI
 

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