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Imaging Analytics and the Changing World of Orthopaedics

Taken together, these benefits yield one additional advantage that is particularly important in today’s uncertain economy: cost savings. Not only is the resulting data quantitative and objective, but the reduction in time and labor translates directly to cost savings. In addition, the high precision and accuracy of quantitative imaging analytics enables biostatisticians to justify a reduction in total number of specimens or patients required to meet statistical significance in preclinical and clinical trials, respectively. Depending on the drug, device or biologics under study, this alone can translate into substantial cost savings.

Algorithm Development, Customization and Optimization
The significance of custom-tailored imaging analytics is best illustrated with an analogy. An optimized image analysis algorithm (or set of algorithms) is akin to designing a hammer for hitting a specific nail. Standard software packages, however, attempt to design a universal hammer to hit every type of nail in existence. As a result, while this latter approach may be effective for a defined set of images (similar to the "training" set of images from which the governing algorithm was generated), in general, results will be largely inconsistent and inaccurate. By tailoring image processing and analysis algorithms (as well as image acquisition protocols) on a study or application-specific basis, an investigator can generate quantitative, accurate and objective data.

A major caveat inherent in the development, customization and optimization of image analysis algorithms is the necessity to staff or contract professionals who are adequately experienced in image analysis and software engineering for biomedical applications. Furthermore, since algorithms are study specific, they must be thoroughly validated using expert guidance and verification (i.e., radiologist or pathologist), cadaver models, phantoms, etc. They must also be combined with proper imaging and analysis controls when utilized in preclinical and clinical research and product development.

Applications in Basic and Clinical Science
There are many applications for imaging analytics in basic and clinical science. As a rule of thumb, any image feature can be quantitatively analyzed if a human observer can visually perceive and describe it. Examples include, but are not limited to:

  • Quantification and automation of 2D histomorphometry (histology) image measurements across 1,000s of specimens (batch analyses)
  • Quantitative spatial co-registration and analysis of patient computed tomography (CT) and magnetic resonance (MR) image data across multiple time points
  • Co-registration and correlation of 2D histology and micro-CT data
  • In vivo quantification of cartilage repair across multiple time points using MR
  • Single cell tracking and quantification of wound healing via cell markers and fluorescence microscopy
  • In vivo measurement of bone in-/on-growth relative to an orthopaedic implant using CT or micro-CT
  • Automated enumeration of positive vs. negative cells following transfection with an experimental gene therapy 

The list could go on and on. What is important to remember is that imaging and imaging analytics are rapidly growing in popularity as their utility in the biomedical community is becoming more commonplace. Although imaging analytics currently plays a large role in many medical fields, few have benefitted more than the field of orthopaedics.

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