Deploying a Deep Learning Model for Fetal Ultrasound Standard Plane Detection in a Real-World Clinical Setting: A Case Study

แนวคิดหลัก

Despite the rapid development of AI models in medical image analysis, their validation in real-world clinical settings remains limited. This study introduces a generic framework for deploying image-based AI models in clinical settings and evaluates the deployment of a deep learning model for fetal ultrasound standard plane detection.

บทคัดย่อ

The authors introduce a generic framework for deploying image-based AI models in real-world clinical settings. The framework is designed to address key challenges such as device output compatibility, low prediction latency, local processing, wireless display, video recording, and ease of use for research code.

Using this framework, the authors deployed a trained deep learning model for fetal ultrasound standard plane detection and evaluated it in real-time sessions with both novice and expert users. The key findings include:

1. Novice users found the model's explanations on the presence of anatomical landmarks helpful, but requested more navigational guidance to reach the standard planes.
2. Expert users used the model's predictions for confirmation rather than relying on it for guidance, highlighting the different use cases for AI tools between novice and expert users.
3. Participants expressed a desire for the prediction results to be displayed directly on the ultrasound machine, and requested a higher frame rate to better match their scanning speed.

These findings underscore the importance of early deployment of AI models in real-world settings, as it can provide valuable insights to guide the refinement of the model and system based on actual user feedback.

สถิติ

The authors measured the time taken to process one video frame using different computational devices, both by running the native research code and using their framework. The results show that the framework introduces a small additional latency of around 0.05-0.06 seconds compared to running the native code directly.

คำพูด

"Almost all participants expressed a desire for the prediction results to be displayed directly on the ultrasound machine."
"P1, P3 & P4 expressed their wish for more navigational support. They acknowledged that a higher frame rate might be helpful, but ultimately it would be ideal if the tool could tell them the direction they should move the probe if they wanted to reach a certain standard plane."
"P7 used the tool for confirmation of thoughts, while P8 tended to rely on self-judgement rather than relying on feedback from PCBM."

ข้อมูลเชิงลึกที่สำคัญจาก

Deployment of Deep Learning Model in Real World Clinical Setting

by Chun... ที่ arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00032.pdf

สอบถามเพิ่มเติม

How can the deployment framework be further improved to better integrate with the clinical workflow and user preferences, such as displaying results directly on the ultrasound machine?

To enhance the integration of the deployment framework with the clinical workflow and user preferences, particularly in displaying results directly on the ultrasound machine, several improvements can be considered:

Direct Integration: Explore options for direct integration with the ultrasound machine's software or interface. This could involve collaboration with ultrasound equipment manufacturers to enable seamless communication between the AI model and the machine.

Customized Display: Develop a module that can overlay the AI model's predictions directly onto the ultrasound image displayed on the machine's screen. This would provide real-time feedback to the operator without the need for additional display devices.

User Interface Optimization: Design a user-friendly interface that allows clinicians to easily toggle between viewing the standard ultrasound image and the AI model's predictions. This interface should be intuitive and non-intrusive to the scanning process.

Frame Rate Optimization: Address the feedback regarding the frame rate by optimizing the system to provide a higher frame rate, enabling smoother and faster feedback to the operator during scanning.

Feedback Mechanism: Implement a feedback mechanism that allows users to provide real-time feedback on the AI model's predictions. This feedback loop can help improve the model's performance and user experience over time.

Training and Support: Provide training and support to clinicians on how to effectively use the AI tool in their workflow. This includes guidance on interpreting the AI model's predictions and integrating them into their decision-making process.

How can the potential challenges and limitations in developing AI-based navigational guidance for fetal ultrasound scanning be addressed?

Developing AI-based navigational guidance for fetal ultrasound scanning poses several challenges and limitations that need to be addressed:

Complexity of Ultrasound Imaging: Ultrasound imaging is dynamic and requires real-time adjustments based on the fetus's position and movements. Developing AI algorithms that can accurately guide clinicians in acquiring standard planes amidst this complexity is challenging.

Variability in Patient Anatomy: Each patient may have unique anatomical variations, making it difficult to create a one-size-fits-all navigational guidance system. AI models need to be robust enough to adapt to these variations.

Regulatory Approval: Implementing AI-based guidance systems in a clinical setting requires regulatory approval and validation to ensure patient safety and efficacy. Meeting these regulatory requirements can be time-consuming and resource-intensive.

User Acceptance and Trust: Clinicians may be hesitant to rely solely on AI guidance for critical tasks like fetal ultrasound scanning. Building user trust and acceptance in the AI system's capabilities is crucial for successful implementation.

To address these challenges, the following strategies can be employed:

Robust Training Data: Train AI models on diverse and comprehensive datasets to account for variability in patient anatomy and scanning conditions.

Iterative Development: Adopt an iterative development approach, involving clinicians in the design and testing phases to gather feedback and refine the navigational guidance system.

Explainable AI: Ensure that the AI system provides transparent explanations for its recommendations, helping clinicians understand and trust the guidance provided.

Clinical Validation: Conduct rigorous clinical validation studies to demonstrate the effectiveness and safety of the AI-based navigational guidance system before widespread implementation.

How can the insights gained from this real-world deployment be applied to the development of AI tools for other medical imaging modalities and clinical applications?

The insights gained from the real-world deployment of AI tools for fetal ultrasound can be valuable in informing the development of AI tools for other medical imaging modalities and clinical applications:

User-Centric Design: Emphasize user-centric design principles to ensure that AI tools are intuitive, user-friendly, and seamlessly integrated into existing clinical workflows across different imaging modalities.

Early Deployment and Feedback: Advocate for early deployment of AI models in real-world settings to gather feedback from users and refine the tools based on actual clinical needs and challenges.

Explainable AI: Prioritize the development of explainable AI models that provide transparent insights into their decision-making process, enhancing user trust and facilitating adoption in clinical practice.

Customization and Adaptability: Design AI tools that can be customized and adapted to different clinical scenarios, patient populations, and imaging requirements, ensuring versatility and applicability across various medical specialties.

Collaboration with Clinicians: Foster collaboration between AI researchers and clinicians to co-create solutions that address specific clinical needs, optimize workflows, and improve patient outcomes through the effective use of AI technologies.