A Deep-Learning Algorithm to Predict Short-Term Progression to Geographic Atrophy on Spectral-Domain Optical Coherence Tomography.

Importance: The identification of patients at risk of progressing from intermediate age-related macular degeneration (iAMD) to geographic atrophy (GA) is essential for clinical trials aimed at preventing disease progression. DeepGAze is a fully automated and accurate convolutional neural network-based deep learning algorithm for predicting progression from iAMD to GA within 1 year from spectral-domain optical coherence tomography (SD-OCT) scans.

Objective: To develop a deep-learning algorithm based on volumetric SD-OCT scans to predict the progression from iAMD to GA during the year following the scan.

Deep Learning in Dermatology: A Systematic Review of Current Approaches, Outcomes, and Limitations.

Artificial intelligence (AI) has recently made great advances in image classification and malignancy prediction in the field of dermatology. However, understanding the applicability of AI in clinical dermatology practice remains challenging owing to the variability of models, image data, database characteristics, and variable outcome metrics. This systematic review aims to provide a comprehensive overview of dermatology literature using convolutional neural networks.

Current Landscape of Generative Adversarial Networks for Facial Deidentification in Dermatology: Systematic Review and Evaluation.

Background: Deidentifying facial images is critical for protecting patient anonymity in the era of increasing tools for automatic image analysis in dermatology.

Objective: The aim of this paper was to review the current literature in the field of automatic facial deidentification algorithms.

Methods: We conducted a systematic search using a combination of headings and keywords to encompass the concepts of facial deidentification and privacy preservation.

A Feasibility Study on Tribological Origins of Knee Acoustic Emissions.

Objective: Considering the knee as a fluid-lubricated system, articulating surfaces undergo different lubrication modes and generate joint acoustic emissions (JAEs). The goal of this study is to compare knee biomechanical signals against synchronously recorded joint sounds and assess the hypothesis that JAEs are attributed to tribological origins.

Methods: JAE, electromyography, ground reaction force signals, and motion capture markers were synchronously recorded from ten healthy subjects while performing two-leg and one-leg squat exercises.

Quantifying Asymmetry Between Medial and Lateral Compartment Knee Loading Forces Using Acoustic Emissions.

Objective: Osteoarthritis is the most common type of knee arthritis that can be affected by excessive and compressive loads and can affect one or more compartments of the knee: medial, lateral, and patellofemoral. The medial compartment tends to be the most vulnerable to injuries and research suggests that a better understanding of the medial to lateral load distribution conditions could provide insights to the quantitative usage of knee compartments in activities of daily life.

Methods: Prior to study in an osteoarthritic clinical population which may present with various complicating anatomical and physiological changes, we investigate knee acoustical emissions of able-bodied individuals during a varying width squat exercise which simulates loading asymmetries that would typically be seen in this clinical population.

Vibration Stimulation as a Non-Invasive Approach to Monitor the Severity of Meniscus Tears.

Musculoskeletal disorders and injuries are one of the most prevalent medical conditions across age groups. Due to a high load-bearing function, the knee is particularly susceptible to injuries such as meniscus tears. Imaging techniques are commonly used to assess meniscus injuries, though this approach suffers from limitations including high cost, need for skilled personnel, and confinement to laboratory or clinical settings.

Estimating Knee Joint Load Using Acoustic Emissions During Ambulation.

Quantifying joint load in activities of daily life could lead to improvements in mobility for numerous people; however, current methods for assessing joint load are unsuitable for ubiquitous settings. The aim of this study is to demonstrate that joint acoustic emissions contain information to estimate this internal joint load in a potentially wearable implementation. Eleven healthy, able-bodied individuals performed ambulation tasks under varying speed, incline, and loading conditions while joint acoustic emissions and essential gait measures-electromyography, ground reaction forces, and motion capture trajectories-were collected.

Fit to Burst: Toward Noninvasive Estimation of Achilles Tendon Load Using Burst Vibrations.

Objective: Tendons are essential components of the musculoskeletal system and, as with any mechanical structure, can fail under load. Tendon injuries are common and can be debilitating, and research suggests that a better understanding of their loading conditions could help mitigate injury risk and improve rehabilitation. To that end, we present a novel method of noninvasively assessing parameters related to mechanical load in the Achilles tendon using burst vibrations.

Acoustic Emissions as a Non-invasive Biomarker of the Structural Health of the Knee.

The longitudinal assessment of joint health is a long-standing issue in the management of musculoskeletal injuries. The acoustic emissions (AEs) produced by joint articulation could serve as a biomarker for joint health assessment, but their use has been limited by a lack of mechanistic understanding of their creation. In this paper, we investigate that mechanism using an injury model in human lower-limb cadavers, and relate AEs to joint kinematics.

Robust Longitudinal Ankle Edema Assessment Using Wearable Bioimpedance Spectroscopy.

Objective: We present a robust methodology for tracking ankle edema longitudinally based on bioimpedance spectroscopy (BIS).

Methods: We designed a miniaturized BIS measurement system and employed a novel calibration method that enables accurate, high-resolution measurements with substantially lower power consumption than conventional approaches. Using this state-of-the-art wearable BIS measurement system, we developed a differential measurement technique for robust assessment of ankle edema.

A Glove-Based Form Factor for Collecting Joint Acoustic Emissions: Design and Validation.

Sounds produced by the articulation of joints have been shown to contain information characteristic of underlying joint health, morphology, and loading. In this work, we explore the use of a novel form factor for non-invasively acquiring acoustic/vibrational signals from the knee joint: an instrumented glove with a fingertip-mounted accelerometer. We validated the glove-based approach by comparing it to conventional mounting techniques (tape and foam microphone pads) in an experimental framework previously shown to reliably alter healthy knee joint sounds (vertical leg press).

-Value: A Potential Biomarker for Assessing Knee-Joint Health Using Acoustical Emission Sensing.

This paper explores the novel application of an automated -value extraction algorithm for the interpretation of sounds produced by the knee joint during movement. Acoustical emissions were recorded from a total of eight subjects with acute knee injuries a first time, within one week of the injury, then a second time, four to six months following corrective surgery and rehabilitation. The data were collected from each subject using miniature electret microphones placed on the medial and lateral side of the patella during knee flexion and extension exercises.

Unobtrusive Heartbeat Detection from Mice Using Sensors Embedded in the Nest.

Unobtrusive monitoring of physio-behavioral variables from animals can minimize variability in preclinical research and thereby maximize the potential for clinical translation. In this paper, we present the design, implementation, and validation of an instrumented nest providing continuous recordings of seismocardiogram (SCG) signals and skin temperature. SCG represents the chest-wall vibrations associated with the heartbeat, and can potentially provide a measure by which individual heartbeats can be detected without the need for electrodes or implantable devices.

Quantifying the Effects of Increasing Mechanical Stress on Knee Acoustical Emissions Using Unsupervised Graph Mining.

In this paper, we investigate the effects of increasing mechanical stress on the knee joints by recording knee acoustical emissions and analyze them using an unsupervised graph mining algorithm. We placed miniature contact microphones on four different locations: on the lateral and medial sides of the patella and superficial to the lateral and medial meniscus. We extracted audio features in both time and frequency domains from the acoustical signals and calculated the graph community factor (GCF): an index of heterogeneity (variation) in the sounds due to different loading conditions enforced on the knee.

Real-time activity classification in a wearable system prototype for knee health assessment via joint sounds.

An algorithm for performing activity classification for a joint health assessment system using acoustical emissions from the knee is presented. The algorithm was refined based on linear acceleration data from the shank and the thigh sampled at 100 Hz/ch and collected from eight healthy subjects performing unloaded flexion-extension and sit-to-stand motions. The algorithm was implemented on a field-programmable gate array (FPGA)-based processor and has been validated in realtime on a subject performing two minutes of activities consisting of flexion-extension, sit-to-stand, and other motions while standing.

Wearable Vector Electrical Bioimpedance System to Assess Knee Joint Health.

Objective: We designed and validated a portable electrical bioimpedance (EBI) system to quantify knee joint health.

Methods: Five separate experiments were performed to demonstrate the: 1) ability of the EBI system to assess knee injury and recovery; 2) interday variability of knee EBI measurements; 3) sensitivity of the system to small changes in interstitial fluid volume; 4) reducing the error of EBI measurements using acceleration signals; and 5) use of the system with dry electrodes integrated to a wearable knee wrap.

Results: 1) The absolute difference in resistance ( R) and reactance (X) from the left to the right knee was able to distinguish injured and healthy knees (p < 0.

Quantifying the Consistency of Wearable Knee Acoustical Emission Measurements During Complex Motions.

Knee-joint sounds could potentially be used to noninvasively probe the physical and/or physiological changes in the knee associated with rehabilitation following acute injury. In this paper, a system and methods for investigating the consistency of knee-joint sounds during complex motions in silent and loud background settings are presented. The wearable hardware component of the system consists of a microelectromechanical systems microphone and inertial rate sensors interfaced with a field programmable gate array-based real-time processor to capture knee-joint sound and angle information during three types of motion: flexion-extension (FE), sit-to-stand (SS), and walking (W) tasks.