Design and Quality for Biomedical Technologies XVIII

Rapid advances in optical technologies and computational power have brought about a revolution in biomedical imaging, sensing and therapeutics. These advances necessitate parallel progress in techniques used for development and evaluation and that enhance rigor and reproducibility and result in safe, effective, and commercially-viable biomedical technologies. This conference will focus on topics that are critical to optimizing design and ensuring quality, including:

1. systems and components which require unique solutions for biomedical applications.
2. evaluation of quality and safety of biomedical imaging devices and technologies.
3. establishment of device reliability, including failure and performance degradation.
4. development and implementation of phantom-based test methods for optical devices in medicine.

This conference provides a unique forum for scientists and engineers from academia, industry and government to discuss design and quality issues relevant to biomedical imaging, sensing and therapeutics. Interactions between these parties will facilitate the dissemination of knowledge and advancement of capabilities for optimization and evaluation of biophotonic technology. Such progress will enhance biomedical knowledge and improve patient care. In recent years, there has been increasing awareness of the need to address racial, ethnic and gender health inequities. Design of systems and processes should account for factors associated with relevant and diverse patient populations to avoid bias toward narrow populations and ensure health equity. Submissions pertaining to optical imaging or sensing, diagnostics and therapeutics for all fields of medicine as well as optical evaluation of pharmaceuticals and biotechnology products are solicited for this conference; we particularly encourage papers addressing unmet needs in women’s health and racial/gender health equity.

I. Design

• optical components and system architecture
• methods for optimizing performance through design
• computational modeling to optimize system design
• design for robustness to patient variables
• design for women’s health applications
• design for health equity in minority populations
• emerging micro-optics and MEMS-based technology
• sources, detectors and other components
• illumination and detection geometry for spectroscopy and imaging
• designing reliable machine learning algorithms
• validation and application of computer-aided design tools
• AI-aided imaging: design and analysis.
  

II. Quality

• Quality by Design (QbD)
• best practices for standardized device testing
• device calibration and intercomparison
• test methods and models for evaluating skin pigmentation
• metrology
• development/evaluation of novel measurement tools
• validation of computer-aided diagnosis algorithms
• critical metrics for assessing quality
• quality, compliance and regulatory issues related to biomedical devices
• statistical approaches for evaluating biophotonic technologies
• patient and user safety; photothermal, photochemical, etc.

III. Reliability

• physics, analysis, failure mechanisms
• testing for failure
• aging, dormancy and component degradation
• computational and analytical modeling for reliability
• determination of factors of safety
• reprocessing and clinical device performance
• AI-aided imaging.

IV. Phantom-based Testing

• standardized image quality testing
• applications of phantoms for performance comparison
• clinical implementation of phantoms, e.g., clinical trial standardization
• phantom-based validation of numerical models
• tissue-mimicking and standard reference materials
• materials and phantoms for women’s health, e.g., cervical/vaginal applications
• materials and phantoms for health equity, e.g., skin pigmentation
• advanced phantom fabrication approaches, e.g., multi-photon 3D printing
• anthropomorphic and biomimetic (functional) phantoms
• dynamic test methods, e.g., for pulse oximetry
• validation of phantom morphology and properties
• reliable optical property characterization
• phantom design, fabrication, and validation procedures
• assessing phantom stability and robustness
• testing and uncertainty analysis
• computational phantoms
• use of phantoms for training AI algorithms.