Introduction We studied the correlation of central macular fluid volume (CMFV) and central subfield thickness (CST) with best-corrected visual acuity (BCVA) in treatment-naïve eyes with diabetic macular edema (DME) 1 month after anti-vascular endothelial growth factor (VEGF) therapy. Methods This retrospective cohort study investigated eyes that received anti-VEGF therapy. All participants underwent comprehensive examinations and optical coherence tomography (OCT) volume scans at baseline (M0) and 1 month after the first treatment (M1). Two deep learning models were separately developed to automatically measure the CMFV and the CST. Correlations were analyzed between the CMFV and the logMAR BCVA at M0 and logMAR BCVA at M1. The area under the receiver operating characteristic curve (AUROC) of CMFV and CST for predicting eyes with BCVA \(\ge\) 20/40 at M1 was analyzed. Results This study included 156 DME eyes from 89 patients. The median CMFV decreased from 0.272 (0.061–0.568) at M0 to 0.096 (0.018–0.307) mm³ at M1. The CST decreased from 414 (293–575) to 322 (252–430) μm. The logMAR BCVA decreased from 0.523 (0.301–0.817) to 0.398 (0.222–0.699). Multivariate analysis demonstrated that the CMFV was the only significant factor for logMAR BCVA at both M0 (β = 0.199, p = 0.047) and M1 (β = 0.279, p = 0.004). The AUROC of CMFV for predicting eyes with BCVA \(\ge\) 20/40 at M1 was 0.72, and the AUROC of CST was 0.69. Conclusions Anti-VEGF therapy is an effective treatment for DME. Automated measured CMFV is a more accurate prognostic factor than CST for the initial anti-VEGF treatment outcome of DME.

Background and Objectives: Diffuse‐tenosynovial giant cell tumor (D‐TGCT) is a rare, locally aggressive, typically benign neoplasm affecting mainly large joints, representing a wide clinical spectrum. We provide a picture of the treatment journey of D‐TGCT patients as a 2‐year observational follow‐up. Methods: The TGCT Observational Platform Project registry was a multinational, multicenter, prospective observational study at tertiary sarcoma centers spanning seven European countries and two US sites. Histologically confirmed D‐TGCT patients were categorized as either those who remained on initial treatment strategy (determined at baseline visit) or those who changed treatment strategy with specific changes documented (e.g., systemic treatment to surgery) at the 1‐year and/or 2‐year follow‐up visits. Results: A total of 176 patients were assessed, mean diagnosis age was 38.4 ( SD ± 14.6) years; most patients had a knee tumor (120/176, 68.2%). For the 2‐year observation period, most patients (75.5%) remained on the baseline treatment strategy throughout, 54/79 patients (68.4%) remained no treatment, 30/45 patients (66.7%) remained systemic treatment, 39/39 patients (100%) remained surgery. Those who changed treatment strategy utilized multimodal treatment options. Conclusions: This is the first prospectively collected analysis to describe D‐TGCT patient treatments over an extended follow‐up and demonstrates the need for multidisciplinary teams to determine an optimal treatment strategy.

Introduction The aim of this work is to develop a deep learning (DL) system for rapidly and accurately screening for intraocular tumor (IOT), retinal detachment (RD), vitreous hemorrhage (VH), and posterior scleral staphyloma (PSS) using ocular B-scan ultrasound images. Methods Ultrasound images from five clinically confirmed categories, including vitreous hemorrhage, retinal detachment, intraocular tumor, posterior scleral staphyloma, and normal eyes, were used to develop and evaluate a fine-grained classification system (the Dual-Path Lesion Attention Network, DPLA-Net). Images were derived from five centers scanned by different sonographers and divided into training, validation, and test sets in a ratio of 7:1:2. Two senior ophthalmologists and four junior ophthalmologists were recruited to evaluate the system's performance. Results This multi-center cross-sectional study was conducted in six hospitals in China. A total of 6054 ultrasound images were collected; 4758 images were used for the training and validation of the system, and 1296 images were used as a testing set. DPLA-Net achieved a mean accuracy of 0.943 in the testing set, and the area under the curve was 0.988 for IOT, 0.997 for RD, 0.994 for PSS, 0.988 for VH, and 0.993 for normal. With the help of DPLA-Net, the accuracy of the four junior ophthalmologists improved from 0.696 (95% confidence interval [CI] 0.684–0.707) to 0.919 (95% CI 0.912–0.926, p < 0.001), and the time used for classifying each image reduced from 16.84 ± 2.34 s to 10.09 ± 1.79 s. Conclusions The proposed DPLA-Net showed high accuracy for screening and classifying multiple ophthalmic diseases using B-scan ultrasound images across mutiple centers. Moreover, the system can promote the efficiency of classification by ophthalmologists.

Contractions of the non-pregnant uterus play a key role in fertility. Yet, the electrophysiology underlying these contractions is poorly understood. In this paper, we investigate the presence of uterine electrical activity and characterize its propagation in unstimulated ex vivo human uteri. Multichannel electrohysterographic measurements were performed in five freshly resected human uteri starting immediately after hysterectomy. Using an electrode grid externally and an electrode array internally, measurements were performed up to 24 h after hysterectomy and compared with control. Up to 2 h after hysterectomy, we measured biopotentials in all included uteri. The median root mean squared (RMS) values of the external measurements ranged between 3.95 μV (interquartile range (IQR) 2.41–14.18 μV) and 39.4 μV (interquartile range (IQR) 10.84–105.64 μV) and were all significantly higher than control (median RMS of 1.69 μV, IQR 1.13–3.11 μV), consisting of chicken breast meat. The RMS values decreased significantly over time. After 24 h, the median RMS (1.27 μV, IQR 0.86–3.04 μV) was comparable with the control (1.69 μV, IQR 1.13–3.11 μV, p = 0.125). The internal measurements showed a comparable pattern over time, but overall lower amplitude. The measured biopotentials propagated over the uterine surface, following both a plane-wave as well as an erratic pattern. No clear pacemaker location nor a preferred propagation direction could be identified. These results show that ex vivo uteri can spontaneously generate propagating biopotentials and provide novel insight contributing to improving our understanding of the electrophysiology of the human non-pregnant uterus.

This study evaluated cone density (CD) in the macular region and assess macular perfusion in children with varying degrees of myopia. This was a prospective, cross-sectional, observational study. Children underwent confocal scanning laser ophthalmoscopy (cSLO), optical coherence tomography (OCT), and OCT angiography (OCTA) imaging. A built-in software was used to measure mean CD (cells/mm²), retinal vessel density, choriocapillaris perfusion area, and choroidal thickness (CT). The study included 140 eyes from children categorized into four groups: emmetropia (31 eyes), low myopia (44 eyes), moderate myopia (31 eyes), and high myopia (34 eyes). The high myopia group exhibited significantly lower macular CD than the emmetropia group (P < 0.05). Additionally, the high myopia group showed thinner CT and higher choriocapillaris perfusion area in the macular region than the emmetropia group (all P < 0.01). Macular CD was significantly correlated with age, spherical equivalent, axial length, and CT (all P < 0.05). Generalized linear models revealed CT as the independent factor associated with macular CD (Wald χ2 = 9.265, P = 0.002). Children with high myopia demonstrate reduced CD in the macular region, accompanied by reduced CT. These findings may have important implications for future myopia prevention and management strategies.

Ultrawidefield fundus (UWF) images have a wide imaging range (200° of the retinal region), which offers the opportunity to show more information for ophthalmic diseases. Image quality assessment (IQA) is a prerequisite for applying UWF and is crucial for developing artificial intelligence-driven diagnosis and screening systems. Most image quality systems have been applied to the assessments of natural images, but whether these systems are suitable for evaluating the UWF image quality remains debatable. Additionally, existing IQA datasets only provide photographs of diabetic retinopathy (DR) patients and quality evaluation results applicable for natural image, neglecting patients’ clinical information. To address these issues, we established a real-world clinical practice ultra-widefield fundus images dataset, with 700 high-resolution UWF images and corresponding clinical information from six common fundus diseases and healthy volunteers. The image quality is annotated by three ophthalmologists based on the field of view, illumination, artifact, contrast, and overall quality. This dataset illustrates the distribution of UWF image quality across diseases in clinical practice, offering a foundation for developing effective IQA systems.