Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model.

Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce RetiZero, a vision-language model that incorporates knowledge from over 400 fundus diseases. The model is pre-trained on 341,896 fundus images with accompanying text descriptions gathered from diverse sources across multiple ethnicities and countries.

BRCA1 expression in ovarian cancer and its relationship with clinicopathological characteristics and prognosis.

To investigate the relationship between breast cancer susceptibility 1 (BRCA1) and clinicopathological characteristics and prognosis of ovarian cancer patients. Cancer tissue samples, paracancerous tissue samples and clinical data of 195 patients with ovarian cancer diagnosed in our hospital from January 2016 to August 2021 were collected and sorted out. The expression of BRCA1 protein in ovarian cancer was retrieved from the Clinical Proteomic Tumor Analysis Consortium(CPTAC) dataset of the Ualcan database; the expression of BRCA1 protein was detected by immunohistochemistry; Kaplan-Meier curve was drawn to analyze the relationship between BRCA1 protein expression level and 3-year survival rate of ovarian cancer patients; multivariate Cox regression analysis was used to examine the prognostic factors of ovarian cancer patients.

Construction of a nomogram prediction model for deep vein thrombosis in epithelial ovarian cancer.

Objective: To develop and validate a nomogram prediction model for deep vein thrombosis (DVT) in epithelial ovarian cancer (EOC).

Methods: Between May 2021 and May 2024, 429 EOC patients admitted to our hospital were retrospectively identified. The patients were randomly divided into a modeling group and a validation group.

Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers.

Ovarian cancer has a high mortality rate, primarily due to late diagnosis and complex pathogenesis. This study develops an integrative prognostic model combining genetic, clinical, and immunological data to predict outcomes in ovarian cancer patients. Utilizing data from The Cancer Genome Atlas (TCGA), we identified significant prognostic genes through differential expression and survival analysis, integrating these with clinical features and immune landscape assessments including immune cell infiltration and checkpoint expression.

Efficiency and safety of automated label cleaning on multimodal retinal images.

Label noise is a common and important issue that would affect the model's performance in artificial intelligence. This study assessed the effectiveness and potential risks of automated label cleaning using an open-source framework, Cleanlab, in multi-category datasets of fundus photography and optical coherence tomography, with intentionally introduced label noise ranging from 0 to 70%. After six cycles of automatic cleaning, significant improvements are achieved in label accuracies (3.

Enhancing AI reliability: A foundation model with uncertainty estimation for optical coherence tomography-based retinal disease diagnosis.

Inability to express the confidence level and detect unseen disease classes limits the clinical implementation of artificial intelligence in the real world. We develop a foundation model with uncertainty estimation (FMUE) to detect 16 retinal conditions on optical coherence tomography (OCT). In the internal test set, FMUE achieves a higher F1 score of 95.

Self-supervised based clustering for retinal optical coherence tomography images.

Background: In response to the inadequacy of manual analysis in meeting the rising demand for retinal optical coherence tomography (OCT) images, a self-supervised learning-based clustering model was implemented.

Methods: A public dataset was utilized, with 83,484 OCT images with categories of choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal fundus. This study employed the Semantic Pseudo Labeling for Image Clustering (SPICE) framework, a self-supervised learning-based method, to cluster unlabeled OCT images into binary and four categories, and the performances were compared with baseline models.

Machine learning analysis of oxidative stress-related phenotypes for specific gene screening in ovarian cancer.

Background: Oxidative stress serves a crucial role in tumor development. However, the relationship between ovarian cancer and oxidative stress remains unknown. We aimed to create an oxidative stress-related prognostic signature to enhance the prognosis prediction of CC patients using bioinformatics.

Coefficient of spatial variance of choroidal thickness on swept-source optical coherence tomography in healthy eyes.

Purpose: This study aims to introduce the coefficient of spatial variance of choroidal thickness to describe the choroidal variation and investigate its associated factors in healthy eyes.

Methods: This retrospective cross-sectional study included 1031 eyes from 1031 subjects who received a swept-source optical coherence tomography examination. The mean choroidal thickness in the macular 6 × 6 mm region and 900 subregions of 0.

Inter-Individual Topographic Variation of Choroidal Thickness in Healthy Eyes on Swept-Source Optical Coherence Tomography.

Purpose: To investigate the topographic characters of inter-individual variations of the macular choroidal thickness (CT).

Methods: This was a retrospective study. Macular CT data for 900 0.

An m1A/m6A/m5C-associated long non-coding RNA signature: Prognostic and immunotherapeutic insights into cervical cancer.

Background: Cervical cancer (CC) remains a significant clinical challenge, even though its fatality rate has been declining in recent years. Particularly in developing countries, the prognosis for CC patients continues to be suboptimal despite numerous therapeutic advances.

Methods: Using The Cancer Genome Atlas database, we extracted CC-related data.

Uncertainty-inspired open set learning for retinal anomaly identification.

Failure to recognize samples from the classes unseen during training is a major limitation of artificial intelligence in the real-world implementation for recognition and classification of retinal anomalies. We establish an uncertainty-inspired open set (UIOS) model, which is trained with fundus images of 9 retinal conditions. Besides assessing the probability of each category, UIOS also calculates an uncertainty score to express its confidence.

Research Trends and Hotspots of Retinal Optical Coherence Tomography: A 31-Year Bibliometric Analysis.

The emergence of optical coherence tomography (OCT) over the past three decades has sparked great interest in retinal research. However, a comprehensive analysis of the trends and hotspots in retinal OCT research is currently lacking. We searched the publications on retinal OCT in the Web of Science database from 1991 to 2021 and performed the co-occurrence keyword analysis and co-cited reference network using bibliometric tools.

Vitreomacular Interface Disorders in Proliferative Diabetic Retinopathy: An Optical Coherence Tomography Study.

Vitreomacular interface plays an important role in the pathogenesis and progression of proliferative diabetic retinopathy (PDR). This study investigated the prevalence and risk factors of vitreomacular interface disorders (VMID) in PDR. The macular optical coherence tomography (OCT) scans of 493 eyes from 378 PDR patients were retrospectively reviewed to detect VMID, including vitreomacular adhesion (VMA), vitreomacular traction (VMT), epiretinal membrane (ERM), lamellar hole-associated epiretinal proliferation (LHEP), and macular hole (MH).

Improved Automated Foveal Avascular Zone Measurement in Cirrus Optical Coherence Tomography Angiography Using the Level Sets Macro.

Purpose: To evaluate automated measurements of the foveal avascular zone (FAZ) using the Level Sets macro (LSM) in ImageJ as compared with the Cirrus optical coherence tomography angiography (OCTA) inbuilt algorithm and the Kanno-Saitama macro (KSM).

Methods: The eyes of healthy volunteers were scanned four times consecutively on the Zeiss Cirrus HD-OCT 5000 system. The FAZ metrics (area, perimeter, and circularity) were measured manually and automatically by the Cirrus inbuilt algorithm, the KSM, and the LSM.

Reliability of foveal avascular zone metrics automatically measured by Cirrus optical coherence tomography angiography in healthy subjects.

Purpose: To investigate the reliability of the foveal avascular zone (FAZ) metrics automatically measured using Cirrus optical coherence tomography angiography (OCTA) embedded algorithm compared to human manual measurement.

Methods: Thirty-five eyes of 35 healthy subjects were enrolled and scanned four times continuously on Zeiss Cirrus HD-OCT 5000. The FAZ metrics (area, circularity and perimeter) of the superficial capillary plexus were measured automatically using the embedded tool and manually measured by the two independent observers using ImageJ.