AI presentation in radiology protocols.pptx

AI radiology

1. Chest AI – radiologist perspective

2. What we do in radiology Receive request form (interdepartmental referral) Accept or reject Process referral Protocol examination Schedule appointment Hoist/transport/interpreter/infection control/renal function Liase with patient regarding appointment Best time/relative Perform examination Standardised protocol Acquire images Send to PACS Report examination Describe radiological findings Synthesise with clinical data to determine diagnosis or differential diagnosis Describe further management Send report to referral HLA 7 signal to Unity (EPR) hospital ICE to GP

3. How images are acquired? Problems: Poor inspiration Movement

4. 4.5 years 2 weeks

5. Interpretation = Report Technique – Name and date, Exposure, rotation, Good expansion, PA/AP Airway – central?, carina angle <90 degrees Lungs – Left vs Right, Opacities, Lucencies Cardiac – size (<50%), mediastinum, silhouette sign Diaphragm - Left higher right, air below, gastric bubble, costo and cardiophrenic angles Bones – correct no ribs (10 posterior; 6 anterior), Lucency , opacities, fracture

6. Sample findings Radiographs differentiate different tissue by ability to absorb x-rays More dense structures (bone) absorb more than less dense structures (lungs) The differences is represent on basis of opacity Opacity vs lucency in lungs vs vs

7. Reporting process – how image is presented on display with clinical information. Tools to enhance image – zoom/pan, adjust. Images arrive on PACS Configurable display Compare with previous similar examinations Manipulate images Magnify Rotate Reverse Standard review process Last check areas Sites of commonly missed abnormalities Dictate report – voice recognition Check report and double check radiology findings

8. Inhaled FB Fixed hyperinflation left LRTI – mucus plug Left upper lobe collapse Round pneumonia Appearances could be cancer Pleural effusion Multiple rib # NAI Pneumonia Right pneumothorax Pneumoperitoneum

9. AI role Accurate identify all abnormalities on chest radiograph Use clinical data to identify relative likelihood of a causative disease Current clinical details Additional clinical details Identify patterns not discernible to human eye ie radiologist

10. Outputs Dependent on person accessing report Radiologist Identify abnormalities esp areas missed by radiologists Relevant normal findings Clinician (referrer) Likelihood of diagnosis Highlight abnormalities Patient Simplified explanation of report Links to trusted websites for further information Non-specialised clinician Simplified explanation of report

11. Summary

12. ARTIFICIAL INTELLIGENCE Branch of computer science devoted to creating systems to perform tasks that ordinarily require human intelligence . Creation of  intelligent machines that work and react like humans

13. Artificial intelligence in radiology: Friend or foe? Fear has been AI would begin to chip away at jobs. While that concern isn’t coming true as yet, radiologists are being urged to accept and incorporate AI into their interpretations. Integrated AI component within the imaging workflow - increase efficiency, reduce errors and achieve objectives Providing trained radiologists with pre-screened images and identified features. Improves their consistency and quality and potentially lowers operating costs . Scientists have shown that few pixels from other image can drastically alter results . Artificial intelligence won’t necessarily replace radiologists, but it will replace radiologists who don’t use artificial intelligence in future.

14. Benefits of AI in Radiology The integration of AI in radiology brings forth a multitude of benefits, revolutionizing the field and positively impacting patient care. Enhanced Diagnostic Accuracy. Increased Efficiency and Workflow Optimization. Triaging studies that need urgent review by radiologists.  Standardization and Consistency. Early Detection and Intervention. Facilitating the communication of urgent results. Research and Innovation. Education and Training.

15. AI Applications in Radiology AI is revolutionizing radiology by offering a wide range of applications that assist in image analysis, diagnosis and patient management. 1. Image Recognition and Classification: AI algorithms can accurately analyze and classify medical images, aiding in the identification of anatomical structures, lesions, and abnormalities. For example, AI can assist in detecting lung nodules, classifying breast lesions, and identifying brain tumors. 2. Computer-Aided Diagnosis (CAD): AI systems can provide diagnostic suggestions based on image analysis, supporting radiologists in making more accurate and informed decisions. CAD systems can assist in detecting early signs of diseases, such as pulmonary embolism or breast cancer, helping in early intervention and improved patient outcomes. 3. Quantitative Image Analysis: AI algorithms can extract quantitative information from medical images, enabling objective measurements and assessments. For instance, AI can analyze tumor size, growth patterns, or tissue characteristics, aiding in treatment planning and response evaluation.

16. 4. Workflow Optimization: AI tools streamline radiology workflows by automating routine tasks, such as image preprocessing, annotation, and report generation. Workflow optimization reduces manual effort, enhances efficiency, and allows radiologists to focus on complex cases and patient care. 5. Predictive Analytics: AI can analyze patient data, including medical images, clinical records, and genetic information, to predict disease outcomes or treatment responses. Predictive analytics can assist in personalized treatment planning, identifying high-risk patients, and optimizing healthcare resources. 6. Image Reconstruction and Enhancement: AI techniques, such as deep learning, can reconstruct medical images from low-quality or incomplete data, improving image quality and aiding in diagnosis. Image enhancement algorithms can enhance details, reduce noise, and improve visualization for better image interpretation. 7. Natural Language Processing (NLP) for Report Generation: AI-powered NLP algorithms can analyze clinical notes and medical reports, extracting relevant information and generating structured reports. NLP enables standardized and efficient report writing, facilitating communication among healthcare professionals and ensuring accurate documentation.

17. Challenges of AI in Radiology While AI has shown remarkable potential in radiology, there are several challenges and limitations that need to be considered. Let's explore the challenges associated with the use of AI in radiology. 1. Need for Ongoing Human Oversight: AI algorithms are powerful tools, but they require human oversight and expertise to ensure accurate interpretation and clinical decision-making. Radiologists and other healthcare professionals play a crucial role in validating AI-generated results, considering clinical context, and making the final diagnosis. Human oversight helps mitigate the risk of false positives, false negatives, and potential misinterpretation of AI-generated findings. 2. Data Quality and Diversity: AI models heavily rely on large and diverse datasets for training and validation. Availability of high-quality, annotated imaging data that represents diverse patient populations can be a challenge. Biases within datasets can lead to biased AI algorithms, impacting the accuracy and generalizability of AI in radiology. 3. Integration with Existing Systems: Integrating AI algorithms seamlessly into existing radiology workflows and picture archiving and communication systems (PACS) can be complex. Ensuring compatibility, data privacy, security and regulatory compliance are crucial considerations during the integration process.

18. 4. Ethical and Legal Concerns: The use of AI in radiology raises ethical and legal concerns regarding patient privacy, informed consent, data security and liability. Clear guidelines and policies need to be established to address these concerns and protect patient rights in the context of AI-driven radiology. 5. Lack of Standardization: The lack of standardized protocols and guidelines for the development, validation and deployment of AI algorithms in radiology poses a challenge. Establishing robust standards for data collection, algorithm performance evaluation and regulatory approval is essential to ensure consistent and reliable AI applications. 6. Overreliance and Misdiagnosis: Overreliance on AI algorithms without appropriate human oversight can increase the risk of misdiagnosis or missed diagnoses. Radiologists must be cautious of potential pitfalls, limitations and uncertainties associated with AI-generated results, ensuring their independent assessment of the findings.

19. Legal and Ethical Considerations of AI in Radiology The use of AI in radiology brings about important legal and ethical considerations that must be carefully addressed. Let's explore some of the key considerations when using AI in radiology. 1. Patient Privacy and Data Security: AI algorithms require access to patient data, including medical images and health records, for training and validation. Strict measures must be in place to ensure patient privacy and data security, adhering to regulations such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation). Encryption, anonymization , access controls, and secure storage are essential to protect patient data and maintain confidentiality. 2. Informed Consent and Transparency: Patients have the right to be informed about the use of AI algorithms in their radiological examinations and the potential implications. Radiologists and healthcare professionals must communicate the role of AI, its limitations, and the possible impact on diagnosis and treatment to obtain informed consent. Transparency in disclosing the involvement of AI in the diagnostic process fosters patient trust and ensures ethical practice. 3. Algorithm Bias and Fairness: AI algorithms can be influenced by biases present in the training data, leading to potential disparities and unfairness in diagnosis and treatment recommendations. Efforts must be made to address algorithmic biases, ensuring fairness across diverse patient populations. Regular monitoring, auditing, and algorithm retraining are necessary to identify and mitigate biases and ensure equitable outcomes. 4. Liability and Accountability: The introduction of AI in radiology raises questions of liability and accountability in the event of errors or adverse outcomes. It is important to establish clear guidelines and frameworks to attribute responsibility, considering the roles of radiologists, developers, healthcare institutions, and regulatory bodies. Defining accountability and implementing appropriate legal frameworks will ensure that patient safety remains a priority in AI-driven radiology .

20. SUMMARY In conclusion, the revolution of AI in radiology has paved the way for remarkable advancements in healthcare. Throughout this presentation, we have explored the benefits/applications, challenges and legal/ethical considerations of AI in radiology. Key Takeaways: AI has the potential to revolutionize radiology by improving accuracy, efficiency, and patient outcomes. The benefits of AI in radiology include improved diagnostic accuracy, faster image interpretation and in research/training. Machine learning and deep learning are the primary types of AI being used in radiology, enabling advanced image analysis and prediction. AI has shown promising results in various modalities, such as X-rays, CT scans and MRI, aiding in the detection and characterization of diseases. Challenges of AI in radiology include the need for human oversight, data quality, integration, ethical considerations, lack of standardization, and the risk of overreliance. Legal and ethical considerations must be addressed to ensure patient privacy, data security, algorithm fairness, and professional responsibility. AI in radiology is of utmost importance as it enhances the capabilities of radiologists, improves diagnostic accuracy, and contributes to better patient outcomes. AI in radiology is transforming the field, empowering radiologists with advanced tools and decision support systems. By embracing the power of AI in radiology and ensuring its responsible integration, we can shape a future where technology and human expertise work hand-in-hand to provide the best possible care for patients. Let us embark on this journey together, harnessing the full potential of AI in radiology for a brighter and healthier future.

21. THANK YOU

22. Outline of ppt - Slides 1. What we do in radiology – How images are produced? Images processed and sent through to computer for reporting. PACS/RIS – giving clinical details. 3D x-ray – CT scans. 2. Reporting process – how image is presented on display with clinical information. Tools to enhance image – zoom/pan, adjust. 3. Dr Morlese document –paediatric radiology example. Difficulties associated with it. 4. Adult Chest xray examples. 5. AI – how it will be helpful and how it should be implemented. Explainable to radiologist with history and data at time of reporting, 6. Output – Test report. 7. Summary – Findings relayed to clinician & Patient via clinician.