Digital Technology Innovations in Preoperative Surgical Planning

1.1 Why Is It Increasingly Difficult to Grow?The Strategic Imperative 8™: Factors Creating Pressure on Growth

1.2 The Strategic Imperative 8™

1.3 The Impact of the Top Three Strategic Imperatives on AI in the Preoperative Surgical Planning Industry

1.4 About the Growth Pipeline Engine™

1.5 Growth Opportunities Fuel the Growth Pipeline Engine™

2.1 Research Methodology

2.2 Key Findings

3.1 Scope of Analysis

3.2 Market Segmentation

3.3 Application Segmentation

3.4 High Healthcare Expenditure and Costly Surgical Procedures are Key Challenges

3.5 Artificial Intelligence has the Potential to Address Preoperative Surgery Challenges

4.1 AI Mimics Intelligent Human Behavior and Analyzes Large and Complex Datasets to Identify Patterns and Make Accurate Predictions

4.2 AI Offers Anatomical Classification, Alignment, and Automation at High Speed with Accuracy

4.3 Growth Drivers for AI Innovation in Preoperative Surgical Planning

4.4 Growth Restraints for AI Innovation in Preoperative Surgical Planning

4.5 AI is Transforming Current Surgical Practices with Advances in Automation, Navigation, Imaging, and Robots

4.6 AI/ML Supports Automating Routine Tasks in Preoperative Surgical Planning Phase to Improve Patient Outcomes and Save Surgery Time and Costs

5.1 AI Technologies Reduce the Surgeon’s Time Spent on Manual Image Analysis and Healthcare Costs, and Improve Patient Outcomes

5.2 AI Technologies Enable Surgeons to Study the Granularity of Anatomical Structure to Improve Preoperative Surgical Planning

5.3 Novel AI Technologies in Research Optimize the Selection of Implants in Preoperative Surgical Planning

5.4 Accuracy of Large Datasets and Data Privacy are Major Challenges Affecting the Implementation of AI in Orthopedics

6.1 AI Technologies Support Surgeons to Identify Patients Requiring Immediate Surgical Intervention and with Preoperative Planning

6.2 Novel AI Technologies in Research Build AI Algorithms and Deep Learning Models to Automate the Segmentation of Brain Structure

6.3 Large Dataset Requirements for Model Training, Slow Program Development, and Regulatory Approvals are Crucial Challenges Affecting AI Implementation in Neurosurgery

6.4 Market Participants Developing AI/ML Technologies Should Evaluate the Features on Various Statistical Measures to Prove Their Utility

7.1 Formus Labs

7.2 PeekMed

7.3 Omniscient Neurotechnology

7.4 Numex GmbH

7.5 Enhatch

8.1 China is the Innovation Hub for AI-based Technologies in Preoperative Surgical Planning

8.2 Key Patents

8.2 Key Patents (continued)

9.1 Growth Opportunity 1: National and International Partnerships Among Institutions to Build Large Databases to Train AI/ML Algorithms

9.1 Growth Opportunity 1: National and International Partnerships Among Institutions to Build Large Databases to Train AI/ML Algorithm (continued)

9.2 Growth Opportunity 2: Data Scientist-Program Developer Community Ecosystem to Build Innovative and Reliable AI/ML Models or Algorithms

9.2 Growth Opportunity 2: Data Scientist-Program Developer Community Ecosystem to Build Innovative and Reliable AI/ML Models or Algorithms (continued)

9.3 Growth Opportunity 3: Building Innovative AI/ML Technologies for Complete Automation in Preoperative Neurosurgery Planning

9.3 Growth Opportunity 3: Building Innovative AI/ML Technologies for Complete Automation in Preoperative Neurosurgery Planning (continued)

10.1 Key Industry Participants

11.1 Your Next Steps

11.2 Why Frost, Why Now?

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