The biomechanical environment within the eye is of interest in both the regulation of intraocular pressure and the loss of retinal ganglion cell axons in glaucomatous optic neuropathy. Unfortunately, this environment is complex and difficult to determine. Here we provide a brief introduction to basic concepts of mechanics (stress, strain, constitutive relationships) as applied to the eye, and then describe a variety of experimental and computational approaches used to study ocular biomechanics. These include finite element modeling, direct experimental measurements of tissue displacements using optical and other techniques, direct experimental measurement of tissue microstructure, and combinations thereof. Thanks to notable technical and conceptual advances in all of these areas, we are slowly gaining a better understanding of how tissue biomechanical properties in both the anterior and posterior segments may influence the development of, and risk for, glaucomatous optic neuropathy. Although many challenging research questions remain unanswered, the potential of this body of work is exciting; projects underway include the coupling of clinical imaging with biomechanical modeling to create new diagnostic tools, development of IOP control strategies based on improved understanding the mechanobiology of the outflow tract, and attempts to develop novel biomechanically-based therapeutic strategies for preservation of vision in glaucoma.
This editorial reviews the background issues, the design, the key achievements, and the follow-up research generated as a result of the PhysioNet/Computing in Cardiology (CinC) Challenge 2014, published in the concurrent focus issue of Physiological Measurement. Our major focus was to accelerate the development and facilitate the comparison of robust methods for locating heart beats in long-term multi-channel recordings. A public (training) database consisting of 151 032 annotated beats was compiled from records that contained ECGs as well as pulsatile signals that directly reflect cardiac activity, and other signals that may have few or no observable markers of heart beats. A separate hidden test data set (consisting of 152 478 beats) is permanently stored at PhysioNet, and a public framework has been developed to provide researchers with the ability to continue to automatically score and compare the performance of their algorithms. A scoring criteria based on the averaging of gross sensitivity, gross positive predictivity, average sensitivity, and average positive predictivity is proposed. The top three scores (as of March 2015) on the hidden test data set were 93.64%, 91.50%, and 90.70%.