To develop consensus terminology in the setting of polypoidal choroidal vasculopathy (PCV) and to develop and validate a set of diagnostic criteria not requiring indocyanine green angiography (ICGA) for differentiating PCV from typical neovascular age-related macular degeneration (nAMD) based on a combination of OCT and color fundus photography findings. Evaluation of diagnostic test results. Panel of retina specialists. As part of the Asia-Pacific Ocular Imaging Society, an international group of experts surveyed and discussed the published literature regarding the current nomenclature and lesion components for PCV, and proposed an updated consensus nomenclature that reflects our latest understanding based on imaging and histologic reports. The workgroup evaluated a set of diagnostic features based on OCT images and color fundus photographs for PCV that may distinguish it from typical nAMD and assessed the performance of individual and combinations of these non-ICGA features, aiming to propose a new set of diagnostic criteria that does not require the use of ICGA. The final recommendation was validated in 80 eyes from 2 additional cohorts. Consensus nomenclature system for PCV lesion components and non-ICGA-based criteria to differentiate PCV from typical nAMD. The workgroup recommended the terms polypoidal lesion and branching neovascular network for the 2 key lesion components in PCV. For the diagnosis of PCV, the combination of 3 OCT-based major criteria (sub-retinal pigment epithelium [RPE] ring-like lesion, en face OCT complex RPE elevation, and sharp-peaked PED) achieved an area under the receiver operating characteristic curve of 0.90. Validation of this new scheme in a separate subset 80 eyes achieved an accuracy of 82%. We propose updated terminology for PCV lesion components that better reflects the nature of these lesions and is based on international consensus. A set of practical diagnostic criteria applied easily to spectral-domain OCT results can be used for diagnosing PCV with high accuracy in clinical settings in which ICGA is not performed routinely.

The efficient and safe delivery of therapeutic drugs, proteins, and nucleic acids are essential for meaningful therapeutic benefits. The field of nanomedicine shows promising implications in the development of therapeutics by delivering diagnostic and therapeutic compounds. Nanomedicine development has led to significant advances in the design and engineering of nanocarrier systems with supra-molecular structures. Smart mesoporous silica nanoparticles (MSNs), with excellent biocompatibility, tunable physicochemical properties, and site-specific functionalization, offer efficient and high loading capacity as well as robust and targeted delivery of a variety of payloads in a controlled fashion. Such unique nanocarriers should have great potential for challenging biomedical applications, such as tissue engineering, bioimaging techniques, stem cell research, and cancer therapies. However, in vivo applications of these nanocarriers should be further validated before clinical translation. To this end, this review begins with a brief introduction of MSNs properties, targeted drug delivery, and controlled release with a particular emphasis on their most recent diagnostic and therapeutic applications.

Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called “Deep Retina.” Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.

Multi-modal large language models (MLLMs) have transformed the landscape of modern healthcare, with automated radiology report generation (RRG) emerging as a cutting-edge application. While 2D MLLM-based RRG has been well established, its utility for 3D medical images remains largely unexplored. In this regard, we curate the 3D-BrainCT dataset (18,885 text-scan pairs) and develop BrainGPT, a clinically visual instruction-tuned (CVIT) model designed for 3D CT RRG. While we notice that the traditional LLM metrics failed to gauge the diagnostic quality of the RRG, we propose feature-oriented radiology task evaluation (FORTE), an evaluation scheme that captures the clinical essence of the generated reports. Here we show that BrainGPT achieves an average FORTE F1-score of 0.71 (degree = 0.661; landmark = 0.706; feature = 0.693, and impression = 0.779) and 74% of BrainGPT-generated reports were indistinguishable from human-written ground truth in a Turing-like test. Together, our work establishes a comprehensive framework encompassing dataset curation, anatomy-aware model fine-tuning, and the development of robust evaluation metrics for the RRG. By sharing our experience in 3D MLLM-based RRG, we aim to accelerate the expedition in human-machine collaboration for next-generation healthcare.

Purpose.: To assess the prevalence and associated risk factors of myopic maculopathy in an elderly Chinese population in Taiwan. Methods.: Population-based, cross-sectional study. A total of 1361 Chinese aged 65 years or older residing in Shihpai, Taipei, Taiwan, underwent a detailed ophthalmic examination. Of the 1361 participants, 1058 subjects had at least one gradable fundus photograph and were recruited for analysis. High myopia was defined as spherical equivalent of less than −6.0 diopter (D) in the phakic eyes or axial length greater than 26.5 mm in pseudophakic or aphakic eyes. Myopic maculopathy was defined as the appearance of lacquer cracks, focal area of deep choroidal atrophy and macular choroidal neovascularization, or geographic atrophy in the presence of high myopia. Results.: The prevalence of high myopia was 4.2% (44/1058). Signs of myopic maculopathy were present in 32 (72.7%) of the 44 high myopics, representing a prevalence of 3.0% (95% confidence interval, 2.0%–4.0%). Subjects with high myopia with myopic maculopathy had higher systolic blood pressure than those without maculopathy (146.4 ± 16.2 mm Hg vs. 127.0 ± 15.9 mm Hg, P = 0.001), and the difference persisted (P = 0.018) after adjustment for age, sex, smoking, body mass index, diastolic blood pressure, educational levels, alcohol drinking, and histories of diabetes or taking anti-hypertension medication. Of the 65 high myopic eyes, eyes with maculopathy had a greater myopic degree (−12.8 ± 5.1 D vs. −7.6 ± 1.5 D, P = 0.001) and poorer corrected visual acuity (logMAR 0.72 ± 0.6 vs. 0.27 ± 0.2, P = 0.001) than those without. Conclusions.: The prevalence of high myopia and myopic maculopathy in this elderly Chinese population group was high. Of the major risk factors examined, high systolic blood pressure may be associated with myopic maculopathy.

Inherited retinal dystrophies (IRDs) are a group of rare eye diseases caused by gene mutations that result in the degradation of cone and rod photoreceptors or the retinal pigment epithelium. Retinal degradation progress is often irreversible, with clinical manifestations including color or night blindness, peripheral visual defects and subsequent vision loss. Thus, gene therapies that restore functional retinal proteins by either replenishing unmutated genes or truncating mutated genes are needed. Coincidentally, the eye’s accessibility and immune-privileged status along with major advances in gene identification and gene delivery systems heralded gene therapies for IRDs. Among these clinical trials, voretigene neparvovec-rzyl (Luxturna), an adeno-associated virus vector-based gene therapy drug, was approved by the FDA for treating patients with confirmed biallelic RPE65 mutation-associated Leber Congenital Amaurosis (LCA) in 2017. This review includes current IRD gene therapy clinical trials and further summarizes preclinical studies and therapeutic strategies for LCA, including adeno-associated virus-based gene augmentation therapy, 11-cis-retinal replacement, RNA-based antisense oligonucleotide therapy and CRISPR-Cas9 gene-editing therapy. Understanding the gene therapy development for LCA may accelerate and predict the potential hurdles of future therapeutics translation. It may also serve as the template for the research and development of treatment for other IRDs.