Domain-adaptation method between acoustic-response data using different insert earphones.

Classifying acoustic responses captured through earphones offers valuable insights into nearby environments, such as whether the earphones are in or out of the ear. However, the performances of classification algorithms often suffer when applied to other devices due to domain mismatches. This study proposes a domain-adaptation method tailored for acoustic-response data from two distinct insert earphone models.

Comparison of Patterns of Structural Progression in Primary Open Angle Glaucoma and Pseudoexfoliation Glaucoma.

Prcis: Primary open angle glaucoma and pseudoexfoliation glaucoma showed different progression patterns of the retinal nerve fiber layer and ganglion cell-inner plexiform layer thinning in OCT-guided progression analysis.

Purpose: To compare the patterns of progression of retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) thinning by guided progression analysis (GPA) of optical coherence tomography (OCT) in primary open angle glaucoma (POAG) and pseudoexfoliation glaucoma (PXG).

Materials And Methods: The progression of RNFL and GCIPL thinning was assessed by the GPA of Cirrus HD-OCT (Carl Zeiss Meditec, Dublin, CA).

Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists.

Background: Although lesion counting is an evaluation method that effectively analyzes facial acne severity, its usage is limited because of difficult implementation.

Objectives: We aimed to develop and validate an automated algorithm that detects and counts acne lesions by type, and to evaluate its clinical applicability as an assistance tool through a reader test.

Methods: A total of 20,699 lesions (closed and open comedones, papules, nodules/cysts, and pustules) were manually labeled on 1213 facial images of 398 facial acne photography sets (frontal and both lateral views) acquired from 258 patients and used for training and validating algorithms based on a convolutional neural network for classifying five classes of acne lesions or for binary classification into noninflammatory and inflammatory lesions.

Diagnostic ability of macular microvasculature with swept-source OCT angiography for highly myopic glaucoma using deep learning.

Macular OCT angiography (OCTA) measurements have been reported to be useful for glaucoma diagnostics. However, research on highly myopic glaucoma is lacking, and the diagnostic value of macular OCTA measurements versus OCT parameters remains inconclusive. We aimed to evaluate the diagnostic ability of the macular microvasculature assessed with OCTA for highly myopic glaucoma and to compare it with that of macular thickness parameters, using deep learning (DL).

Demographics Prediction and Heatmap Generation From OCT Images of Anterior Segment of the Eye: A Vision Transformer Model Study.

Purpose: To predict demographic characteristics from anterior segment optical coherence tomography (AS-OCT) images of eyes using a Vision Transformer (ViT) model.

Methods: A total of 2970 AS-OCT images were used to train, validate, and test a ViT to predict age and sex, and 2616 images were used for height, weight, and body mass index (BMI). The main outcome measure was the area under the receiver operating characteristic curve (AUC) of the ViT.

Patterns of Optical Coherence Tomography Imaging in Preperimetric Open Angle Glaucoma: A Comparative Study With Young-Age-Onset and Old-Age-Onset Eyes.

Prcis: Optic coherence tomography imaging in preperimetric open angle glaucoma (OAG) differed between young-age-onset and old-age-onset eyes. Inferior and superior quadrants were thinner in young and old-age-onset eyes, respectively. Understanding the specific patterns of early glaucomatous damage based on age-at-onset may improve glaucoma diagnosis and monitoring.

Predicting demographic characteristics from anterior segment OCT images with deep learning: A study protocol.

Introduction: Anterior segment optical coherence tomography (AS-OCT) is a non-contact, rapid, and high-resolution in vivo modality for imaging of the eyeball's anterior segment structures. Because progressive anterior segment deformation is a hallmark of certain eye diseases such as angle-closure glaucoma, identification of AS-OCT structural changes over time is fundamental to their diagnosis and monitoring. Detection of pathologic damage, however, relies on the ability to differentiate it from normal, age-related structural changes.

Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease.

Background: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parkinson's disease (PD). Accurate positioning of the stimulation electrodes is necessary for better clinical outcomes.

Objective: We applied deep learning techniques to microelectrode recording (MER) signals to better predict motor function improvement, represented by the UPDRS part III scores, after bilateral STN DBS in patients with advanced PD.

Deep-learning-based enhanced optic-disc photography.

Optic-disc photography (ODP) has proven to be very useful for optic nerve evaluation in glaucoma. In real clinical practice, however, limited patient cooperation, small pupils, or media opacities can limit the performance of ODP. The purpose of this study was to propose a deep-learning approach for increased resolution and improved legibility of ODP by contrast, color, and brightness compensation.

Dual-input convolutional neural network for glaucoma diagnosis using spectral-domain optical coherence tomography.

Background/aims: To evaluate, with spectral-domain optical coherence tomography (SD-OCT), the glaucoma-diagnostic ability of a deep-learning classifier.

Methods: A total of 777 Cirrus high-definition SD-OCT image sets of the retinal nerve fibre layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) of 315 normal subjects, 219 patients with early-stage primary open-angle glaucoma (POAG) and 243 patients with moderate-to-severe-stage POAG were aggregated. The image sets were divided into a training data set (252 normal, 174 early POAG and 195 moderate-to-severe POAG) and a test data set (63 normal, 45 early POAG and 48 moderate-to-severe POAG).

Macular Ganglion Cell-Inner Plexiform Layer Thickness Prediction from Red-free Fundus Photography using Hybrid Deep Learning Model.

We developed a hybrid deep learning model (HDLM) algorithm that quantitatively predicts macular ganglion cell-inner plexiform layer (mGCIPL) thickness from red-free retinal nerve fiber layer photographs (RNFLPs). A total of 789 pairs of RNFLPs and spectral domain-optical coherence tomography (SD-OCT) scans for 431 eyes of 259 participants (183 eyes of 114 healthy controls, 68 eyes of 46 glaucoma suspects, and 180 eyes of 99 glaucoma patients) were enrolled. An HDLM was built by combining a pre-trained deep learning network and support vector machine.

Automated Quantification of Macular Ellipsoid Zone Intensity in Glaucoma Patients: the Method and its Comparison with Manual Quantification.

The macular ellipsoid zone intensity (mEZi) is a known marker of disease severity in a number of diverse ocular diseases. The purpose of this study was to establish an automated method (AM) for mEZi quantification and to compare the method's performance with that of a manual method (MM) for glaucoma patients and healthy controls. Seventy-one (71) mild-to-moderate glaucoma patients, 71 severe-glaucoma patients, and 51 controls were enrolled.

Bidirectional Recurrent Auto-Encoder for Photoplethysmogram Denoising.

Photoplethysmography (PPG) has become ubiquitous with the development of smart watches and the mobile healthcare market. However, PPG is vulnerable to various types of noises that are ever present in uncontrolled environments, and the key to obtaining meaningful signals depends on successful denoising of PPG. In this context, algorithms have been developed to denoise PPG, but many were validated in controlled settings or are reliant on multiple steps that must all work correctly.

Brain stimulation patterns emulating endogenous thalamocortical input to parvalbumin-expressing interneurons reduce nociception in mice.

Background: The bursting pattern of thalamocortical (TC) pathway dampens nociception. Whether brain stimulation mimicking endogenous patterns can engage similar sensory gating processes in the cortex and reduce nociceptive behaviors remains uninvestigated.

Objective: We investigated the role of cortical parvalbumin expressing (PV) interneurons within the TC circuit in gating nociception and their selective response to TC burst patterns.