112009006_MI_presentation - Om Chate.pdf

AI enhanced Ortho diagnosis

1. 112009006 OM CHATE
AI-ENHANCED
ORTHO DIAGNOSIS
College Of Engineering Pune
Technological University

2. Topic Outline
Introduction
Complexity Of Condition
Challenges
Innovative Solutions
Feasibility of Solutions
Conclusion
TODAY'S
DISCUSSION

3. INTRODUCTION
Orthopedic conditions present diverse
challenges in diagnosis.
Accurate diagnosis is critical for effective
treatment plans.
Complex presentations demand thorough
examination.
Misdiagnosis can lead to poor outcomes.
Precise diagnosis enhances patient care.

4. Complexity of Conditions
Orthopedic conditions exhibit diverse complexities and presentations.
Symptom overlap complicates differentiation between conditions.
Misinterpretation of symptoms may lead to incorrect treatment strategies.
Delayed diagnosis can exacerbate patients' conditions.
Complexity increases the risk of oversight or underestimation of conditions.
Patient outcomes and recovery timelines may be adversely affected.

5. Challenge 1
Expertise Dependency:
Dependence on expertise
for accurate interpretation.
Lack of specialized
knowledge may lead to
errors.
Challenge 3
Risk of Oversight:
Subtle abnormalities may
go unnoticed.
Overlooking important
findings impacts patient
care.
Challenge 2
Potential for Misinterpretation:
Imaging prone to
misinterpretation due to
complexity.
Errors can result in
incorrect diagnoses or
treatment plans.
Challenge 4
Consequences of Inadequate
Interpretation:
Incorrect diagnoses may
lead to unnecessary
procedures.
Challenges

6. AI-Assisted Diagnosis
AI algorithms can analyze imaging data
to aid in diagnosis.
AI can highlight abnormalities and
patterns in imaging, assisting
orthopedic doctors in making more
accurate diagnosis.
Decision Support Systems
Decision support systems integrate
patient history, symptoms, and imaging
results to assist in diagnosis.
These systems provide guidance and
recommendations to orthopedic
doctors, aiding in arriving at accurate
diagnosis.
INNOVATIVE
SOLUTIONS

7. AI-Assisted Diagnosis
Utilizes a large dataset of X-rays, MRI, and CT scans for training the model
Integrates information from multiple imaging modalities for comprehensive analysis.
Automatically identifies abnormalities, lesions, fractures, and other pathologies in images.
Recognizes subtle patterns indicative of musculoskeletal conditions,
such as bone deformities or soft tissue injuries.
Classifies images into different diagnostic categories (e.g., fractures, tumors, arthritis).
Provides real-time feedback to radiologists or orthopedic doctors during image interpretation.

8. Using Random Forest Algorithm for Decision Support Systems:
Data Integration: Integrates patient history, symptoms, and imaging results.
Machine Learning: Utilizes ensemble learning to handle complex datasets.
Personalized Recommendations: Tailors recommendations based on individual
patient features.
Risk Assessment: Predicts likelihood of complications or adverse outcomes.
Continuous Learning: Updates and refines predictions based on new data.
Clinical Integration: Seamlessly integrates into existing clinical workflows.
Decision Support Systems

9. Feasibility of Solutions
Integration into Practice
Adoption of AI-assisted diagnosis and decision support systems requires seamless integration into existing clinical workflows.
Training and education programs are essential to ensure orthopedic doctors can effectively utilize these technology
Data Security and Privacy
Safeguarding patient data and ensuring compliance with privacy regulations is paramount when implementing AI and
decision support systems.
Robust security measures should be in place to protect sensitive medical information from unauthorized access or
breaches.

10. Conclusions
Diagnosis challenges in orthopedics stem from the complexity of conditions and
the interpretation of imaging studies.
AI-assisted diagnosis and decision support systems offer promising solutions to
improve accuracy and efficiency in diagnosing orthopedic conditions.
Continued advancements in AI technology and decision support systems will
further enhance diagnostic capabilities in orthopedics.
Ongoing education and training for orthopedic doctors will be essential to
maximize the benefits of these technologies and ensure optimal patient care.

11. THANK
YOU!