Pelvic floor disorders are caused by weakening or damage to the tissues lining the bottom of the abdominal cavity. These disorders affect nearly 1 in every 4 women in the United States and symptoms that drastically diminish a patient's quality of life. Vaginal closure force is a good measure of pelvic health, but current vaginal dynamometers were not designed for the rigors of hospital reprocessing, often failing due to sensor degradation through repeated sterilization processes. In order to obtain measurements of vaginal closure force in a large study, we designed a vaginal dynamometer that utilizes a removable intra-abdominal sensor already in production for the study. The sensor's existing data acquisition system was modified to transmit to a tablet allowing the user to view data in real-time. The new speculum design allowed a single sensor to measure vaginal closure force before being used to collect intra-abdominal pressure data in the same study visit. The measurements taken with the new speculum were similar to measurements taken with a previously reported vaginal dynamometer.

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Light-scattering spectroscopy (LSS) is an established optical approach for characterization of biological tissues. Here, we investigated the capabilities of LSS and convolutional neural networks (CNNs) to quantitatively characterize the composition and arrangement of cardiac tissues. We assembled tissue constructs from fixed myocardium and the aortic wall with a thickness similar to that of the atrial free wall. The aortic sections represented fibrotic tissue. Depth, volume fraction, and arrangement of these fibrotic insets were varied. We gathered spectra with wavelengths from 500–1100 nm from the constructs at multiple locations relative to a light source. We used single and combinations of two spectra for training of CNNs. With independently measured spectra, we assessed the accuracy of the CNNs for the classification of tissue constructs from single spectra and combined spectra. Combined spectra, including the spectra from fibers distal from the illumination fiber, typically yielded the highest accuracy. The maximal classification accuracy of the depth detection, volume fraction, and permutated arrangements was (mean ± standard deviation (stddev)) 88.97 ± 2.49%, 76.33 ± 1.51%, and 84.25 ± 1.88%, respectively. Our studies demonstrate the reliability of quantitative characterization of tissue composition and arrangements using a combination of LSS and CNNs. The potential clinical applications of the developed approach include intraoperative quantification and mapping of atrial fibrosis, as well as the assessment of ablation lesions.