Search Results (227)

As the prevalence of related diseases continues to rise, a corresponding increase in the demand for internal limiting membrane (ILM) peeling surgery has been observed. However, significant challenges are encountered in ILM peeling surgery, including limited force feedback, inadequate depth perception, and surgeon hand tremors. Research on fully autonomous ILM peeling surgical robots has been conducted to address the imbalance between medical resource availability and patient demand while enhancing surgical safety. An automatic control framework for break initiation in ILM peeling is proposed in this study, which integrates model-predictive control with fractional-order admittance control. Additionally, a multi-vision task surgical scene perception method is introduced based on target detection, key point recognition, and sparse binocular matching. A surgical trajectory planning strategy for break initiation in ILM peeling aligned with operative specifications is proposed. Finally, validation experiments for automatic break initiation in ILM peeling were performed using eye phantoms. The results indicated that the positional error of the micro-forceps tip remained within 40 μm. At the same time, the contact force overshoot was limited to under 6%, thereby ensuring both the effectiveness and safety of break initiation during ILM peeling. Full article

Background/Objectives: Cognitive impairment and vestibular dysfunction commonly co-occur in older adults and may share overlapping neuroanatomical pathways. Understanding their association may enhance the early identification of cognitive decline using clinically feasible vestibular assessments. This study aimed to examine the relationship between vestibular dysfunction and early cognitive impairment, assess the diagnostic accuracy of vestibular markers, and explore the association of subjective dizziness and balance measures with cognitive performance. Methods: Our cross-sectional study included 90 participants aged ≥60 years, classified into cognitively healthy, mild cognitive impairment (MCI), and early Alzheimer’s disease (AD) groups. Cognitive function was assessed using the MoCA and the MMSE; vestibular function was evaluated via posturography sway and horizontal vHIT gain. Subjective dizziness and balance were measured using the Dizziness Handicap Inventory (DHI), gait speed, and eyes-closed balance time. The data were analyzed using SPSS v24 with ANOVA, Pearson correlations, linear regression, and ROC curve analyses. Results: Significant group differences were found across the cognitive and vestibular scores (MoCA: p = 0.001. Sway: p = 0.001. vHIT: p = 0.001). vHIT gain and posturography sway independently predicted the MoCA and MMSE scores (adjusted R² = 0.68 and 0.65, respectively). The ROC analysis showed a strong diagnostic accuracy for posturography sway (AUC = 0.87) and vHIT gain (AUC = 0.82). Conclusions: Vestibular dysfunction is significantly associated with early cognitive impairment and may serve as a useful clinical marker for cognitive screening in older adults. Full article

Background/Objectives: Diabetic retinopathy (DR) remains one of the primary causes of preventable vision impairment worldwide, particularly among individuals with long-standing diabetes. The progressive damage of retinal microvasculature can lead to irreversible blindness if not detected and managed at an early stage. Therefore, the development of reliable, non-invasive, and automated screening tools has become increasingly vital in modern ophthalmology. With the evolution of medical imaging technologies, Optical Coherence Tomography (OCT) has emerged as a valuable modality for capturing high-resolution cross-sectional images of retinal structures. In parallel, machine learning has shown considerable promise in supporting early disease recognition by uncovering complex and often imperceptible patterns in image data. Methods: This study introduces a novel framework for the early detection of DR through multifractal analysis of OCT images. Multifractal features, extracted using a box-counting approach, provide quantitative descriptors that reflect the structural irregularities of retinal tissue associated with pathological changes. Results: A comparative evaluation of several machine learning algorithms was conducted to assess classification performance. Among them, the Multi-Layer Perceptron (MLP) achieved the highest predictive accuracy, with a score of $98.02\%$, along with precision, recall, and F1-score values of $98.24\%$, $97.80\%$, and $98.01\%$, respectively. Conclusions: These results highlight the strength of combining OCT imaging with multifractal geometry and deep learning methods to build robust and scalable systems for DR screening. The proposed approach could contribute significantly to improving early diagnosis, clinical decision-making, and patient outcomes in diabetic eye care. Full article

Background: This study explored the diagnostic potential of image-processing analysis in optical coherence tomography (OCT) images to detect systemic vascular changes in individuals with systemic diseases. Methods: Ocular OCT images from two cohorts diabetic patients and healthy control subjects were analyzed. A novel Superpixel Segmentation (SpS) algorithm was used to process these images and extract optical image density information from ocular vascular tissue. The algorithm was applied to isolate the choroid layer for analysis of its optical properties. The procedure was performed by separate examiners, and both inter- and intra-observer repeatability were assessed. Choroidal area (CA) and choroidal optical image density (COID) metrics were used to assess structural changes in the vascular tissue and predict alterations in the choroidal parameters. Results: A total of 110 diabetic patient eye images and 92 healthy control images were processed. The results showed significant differences in CA and COID between diabetic and healthy eyes, indicating that these parameters could serve as valuable biomarkers for early vascular damage. Conclusions: The use of the SpS algorithm on OCT B-scan images allows for the identification of new parameters linked to ocular vascular damage. These findings suggest that digital image-processing techniques can reveal differences in vascular tissue, offering potential new indicators of pathology. Full article

Background/Objectives: Glioblastoma (GBM) is a highly aggressive primary central nervous system tumor with a median survival of 14 months. MGMT (O6-methylguanine-DNA methyltransferase) promoter methylation status is a key biomarker as a prognostic indicator and a predictor of chemotherapy response in GBM. Patients with MGMT methylated disease progress later and survive longer (median survival rate 22 vs. 15 months, respectively) as compared to patients with MGMT unmethylated disease. Patients with GBM undergo an MRI of the brain prior to diagnosis and following surgical resection for radiation therapy planning and ongoing follow-up. There is currently no imaging biomarker for GBM. Studies have attempted to connect MGMT methylation status to MRI imaging appearance to determine if brain MRI can be leveraged to provide MGMT status information non-invasively and more expeditiously. Methods: Artificial intelligence (AI) can identify MRI features that are not distinguishable to the human eye and can be linked to MGMT status. We employed the UPenn-GBM dataset patients for whom methylation status was available (n = 146), employing a novel radiomic method grounded in hybrid feature selection and weighting to predict MGMT methylation status. Results: The best MGMT classification and feature selection result obtained resulted in a mean accuracy rate value of 81.6% utilizing 101 selected features and five-fold cross-validation. Conclusions: This compared favorably with similar studies in the literature. Validation with external datasets remains critical to enhance generalizability and propagate robust results while reducing bias. Future directions include multi-channel data integration with radiomic features and deep and ensemble learning methods to improve predictive performance. Full article

Gliomas are the most prevalent and aggressive form of primary brain tumors. The clinical challenge in managing patients with this disease revolves around the difficulty of diagnosis, both at onset and during treatment, and the scarcity of prognostic outcome indicators. Radiomics involves the extraction of quantitative features from medical images with the help of artificial intelligence, positioning it as a promising tool to be integrated into the care of glioma patients. Using data from 52 studies and 12,482 patients over two years, this review explores how radiomics can enhance the initial diagnosis of gliomas, especially helping to differentiate treatment stages that may be difficult for the human eye to do otherwise. Radiomics has also been able to identify patient-specific tumor molecular signatures for targeted treatments without the need for invasive surgical biopsy. Such an approach could lead to earlier interventions and more precise individualized therapies that are tailored to each patient. Additionally, it could be integrated into clinical practice to improve longitudinal diagnosis during treatment and predict tumor recurrence. Finally, radiomics has the potential to predict clinical outcomes, helping both patients and providers set realistic expectations. While this field is continuously evolving, future research should conduct such studies in larger, multi-institutional cohorts to enhance generalizability and applicability in clinical practice and focus on combining radiomics with other modalities to improve its predictive accuracy and clinical utility. Full article

Acetylcholinesterase (AChE) is a critical enzyme involved in neurotransmission by hydrolyzing acetylcholine at the synaptic cleft, making it a key target for drug discovery, particularly in the treatment of neurodegenerative disorders such as Alzheimer’s disease. Computational approaches, particularly molecular docking and molecular dynamics (MD) simulations, have become indispensable tools for identifying and optimizing AChE inhibitors by predicting ligand-binding affinities, interaction mechanisms, and conformational dynamics. This review serves as a comprehensive guide for future research on AChE using molecular docking and MD simulations. It compiles and analyzes studies conducted over the past five years, providing a critical evaluation of the most widely used computational tools, including AutoDock, AutoDock Vina, and GROMACS, which have significantly contributed to the advancement of AChE inhibitor screening. Furthermore, we identify PDB ID: 4EY7, the most frequently used AChE crystal structure in docking studies, and highlight Donepezil, a well-established reference molecule widely employed as a control in computational screening for novel inhibitors. By examining these key aspects, this review aims to enhance the accuracy and reliability of virtual screening approaches and guide researchers in selecting the most appropriate computational methodologies. The integration of docking and MD simulations not only improves hit identification and lead optimization but also provides deeper mechanistic insights into AChE–ligand interactions, contributing to the rational design of more effective AChE inhibitors. Full article

Background: Anti-Vascular Endothelial Growth Factor (VEGF) therapy is an effective therapy for improving and stabilizing the vision of patients with neovascular age-related macular degeneration (nAMD). However, the treatment requirements, particularly the number of intraocular injections, can vary significantly among patients. This study aimed to analyze the vascular characteristics of macular neovascularizations (MNVs) to identify potential biomarkers that could predict the required injection frequency throughout the disease course. Methods: In all patients, the initial diagnosis of nAMD was confirmed using optic coherence tomography (OCT), fluorescein angiography, and OCT angiography (OCTA). MNVs detected using OCTA were subjected to quantitative vascular analysis of their area, total vascular length (sumL), fractal dimension (FD), and flow density. These results were then correlated with the number of intravitreal anti-VEGF treatments administered during the first 3 years of treatment. Additionally, the relationship between the parameters and visual acuity progression was analyzed. Results: A total of 68 treatment-naïve eyes were included in the study, comprising 31 eyes with type 1 MNV, 19 eyes with type 2 MNV, and 18 eyes with type 3 MNV. The average MNV area at baseline was 1.11 mm² ± 1.18 mm², the mean total vascular length was 12.95 mm ± 14.24 mm, the mean fractal dimension was 1.26 ± 0.14, and the mean flow density was 41.19 ± 5.87. On average, patients in our cohort received 19.8 ± 8.5 intravitreal injections (IVIs). A significant correlation was found between the number of administered IVIs in the first 3 treatment years and the MNV area (p < 0.005), sumL (p < 0.005), and FD (p < 0.05), while no correlation was found with flow density. Additionally, there was no significant association between MNV type and treatment requirements, nor between MNV vascular architecture and visual acuity progression. Conclusions: The results suggest that the specific vascular structure of untreated MNV may serve as a predictor of long-term treatment demand. With the emergence of new drug classes and advancements in imaging techniques, these parameters could offer valuable insights for forecasting treatment requirements. Full article

Backgrounds: Photodynamic therapy (PDT) is an established treatment modality in central serous chorioretinopathy (CSCR). The goal of our study was to evaluate the morphological and functional effects of PDT in patients with long-lasting CSCR and determine the related predictive factors for improvement. Methods: This retrospective analysis included consecutive patients with chronic CSCR who consented to PDT. The material comprised 98 eyes of 81 patients (67 males and 14 females) with a disease duration longer than 6 months followed for 6 months post treatment. All patients underwent a basic ophthalmological examination including best corrected visual acuity (BCVA) testing and imaging, spectral-domain optical coherence tomography (SD-OCT), and fluorescein angiography. Patients without macular neovascularization (MNV) were subjected to half-dose PDT (3 mg/m²) with standard fluence (50 J/cm²), guided by indocyanine green angiography. Cases complicated by MNV were subjected to full-dose PDT. Results: A morphological response, defined as complete resolution of subretinal fluid, was achieved in 76.29% of cases, and an improvement in BCVA of at least one logMAR line was obtained in 77.53% of cases. The mean BCVA gain was 1.2 logMAR line. All SD-OCT measurements (central retinal thickness, macular volume, mean subfield thickness, subretinal fluid height, and subfoveal choroidal thickness) showed a significant reduction post PDT. A multivariate analysis proved better morphological outcome associations with a younger age and male gender and better visual gains achieved in patients without intraretinal abnormalities. Univariate testing also showed strong relationships between better baseline BCVA and greater functional and morphological improvements, between shorter disease duration and morphological gains, and between the absence of MNV or intraretinal abnormalities and morphological gains. PDT was highly effective in providing a resolution of pigment epithelial detachment (p = 0.0004). The observed effect was significantly dependent upon the lower baseline central retinal thickness (p = 0.0095). Patients with intraretinal abnormalities or MNV showed moderate improvements post PDT. Conclusions: PDT in long-lasting CSCR cases provides good morphological results but generally minor visual gains. Patients’ expectations of significant increases in BCVA after prolonged disease with distinct alterations of the neurosensory retina should be managed. Full article

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is recognized as a precursor to neurodegenerative diseases. This study aimed to develop predictive models for the timing and subtype of phenoconversion in iRBD. We analyzed comprehensive clinical data from 178 individuals with iRBD over a median follow-up of 3.6 years and applied machine learning models to predict when phenoconversion would occur and whether progression would present with motor- or cognition-first symptoms. During follow-up, 30 patients developed a neurodegenerative disorder, and the extreme gradient boosting survival embeddings–Kaplan neighbors (XGBSE-KN) model demonstrated the best performance for timing (concordance index: 0.823; integrated Brier score: 0.123). Age, antidepressant use, and Movement Disorder Society–Unified Parkinson’s Disease Rating Scale Part III scores correlated with higher phenoconversion risk, while coffee consumption was protective. For subtype classification, the RandomForestClassifier achieved the highest performance (Matthews correlation coefficient: 0.697), indicating that higher Montreal Cognitive Assessment scores and younger age predicted motor-first progression, whereas longer total sleep time was associated with cognition-first outcomes. These findings highlight the utility of machine learning in guiding prognosis and tailored interventions for iRBD. Future research should include additional biomarkers, extend follow-up, and validate these models in external cohorts to ensure generalizability. Full article

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia characterized by loss of muscle atonia and abnormal behaviors occurring during REM sleep. Idiopathic RBD (iRBD) is recognized as the strongest prodromal hallmark of α-synucleinopathies, with an established conversion rate to a neurodegenerative condition that reaches up to 96.6% at 15 years of follow-up. Moreover, RBD-converters display a more severe clinical trajectory compared to those that do not present with RBD. However, the extent to which iRBD represents a distinct genetic entity or an early manifestation of neurodegeneration remains unclear. To address this, we applied Genomic Structural Equation Modeling (GenomicSEM) using a GWAS-by-subtraction approach to disentangle the genetic architecture of iRBD from the shared genomic liability across α-synucleinopathies. Our findings highlight the SNCA locus as a key genetic regulator of iRBD susceptibility. While iRBD exhibits a partially distinct genetic signature, residual genomic overlap with neurodegenerative traits suggests that its genetic architecture exists along a continuum of α-synucleinopathy risk. In this scenario, the associations with neuroanatomical correlates may serve as early indicators of a trajectory toward future neurodegeneration. These findings provide a framework for identifying biomarkers that could aid in disease stratification and risk prediction, potentially improving early intervention strategies. Full article

The approach for the diagnosis and treatment of interstitial lung diseases (ILDs) has changed in recent years, mainly for the identification of new entities, such as interstitial lung abnormalities (ILAs) and progressive pulmonary fibrosis (PPF). Clinicians and radiologists are facing new challenges for the screening, diagnosis, prognosis, and follow-up of ILDs. The detection and classification of ILAs or the identification of fibrosis progression at high-resolution computed tomography (HRCT) is difficult, with high inter-reader variability, particularly for non-expert radiologists. In the last few years, various software has been developed for ILD evaluation at HRCT, with excellent results, equal to or more reliable than humans. AI tools can classify ILDs, quantify the extent, analyze the features hidden from the human eye, predict prognosis, and evaluate the progression of the disease. More advanced tools can incorporate clinical and radiological data to obtain personalized prognosis, with the potential ability to steer treatment decisions. To step forward and implement in daily practice such tools, more collaboration is required to collect more homogeneous clinical and radiological data; furthermore, more robust, prospective trials, with the new AI-derived biomarkers compared with each other, are needed to demonstrate the real reliability of the computer-aided evaluation of ILDs. Full article

Eye illness detection is important, yet it can be difficult and error-prone. In order to effectively and promptly diagnose eye problems, doctors must use cutting-edge technologies. The goal of this research paper is to develop a sophisticated model that will help physicians detect different eye conditions early on. These conditions include age-related macular degeneration (AMD), diabetic retinopathy, cataracts, myopia, and glaucoma. Common eye conditions include cataracts, which cloud the lens and cause blurred vision, and glaucoma, which can cause vision loss due to damage to the optic nerve. The two conditions that could cause blindness if treatment is not received are age-related macular degeneration (AMD) and diabetic retinopathy, a side effect of diabetes that destroys the blood vessels in the retina. Problems include myopic macular degeneration, glaucoma, and retinal detachment—severe types of nearsightedness that are typically defined as having a refractive error of –5 diopters or higher—are also more likely to occur in people with high myopia. We intend to apply a user-friendly approach that will allow for faster and more efficient examinations. Our research attempts to streamline the eye examination procedure, making it simpler and more accessible than traditional hospital approaches. Our goal is to use deep learning and machine learning to develop an extremely accurate model that can assess medical images, such as eye retinal scans. This was accomplished by using a huge dataset to train the machine learning and deep learning model, as well as sophisticated image processing techniques to assist the algorithm in identifying patterns of various eye illnesses. Following training, we discovered that the CNN, VggNet, MobileNet, and hybrid Deep Learning models outperformed the SVM and Random Forest machine learning models in terms of accuracy, achieving above 98%. Therefore, our model could assist physicians in enhancing patient outcomes, raising survival rates, and creating more effective treatment plans for patients with these illnesses. Full article

(1) Background. Eye movement abnormalities are increasingly recognized as early biomarkers of Parkinson’s disease (PD), reflecting both motor and cognitive dysfunction. Advances in eye-tracking technology provide objective, quantifiable measures of saccadic impairments, fixation instability, smooth pursuit deficits, and pupillary changes. These advances offer new opportunities for early diagnosis, disease monitoring, and neurorehabilitation. (2) Objective. This narrative review explores the relationship between oculomotor dysfunction and PD pathophysiology, highlighting the potential applications of eye tracking in clinical and research settings. (3) Methods. A comprehensive literature review was conducted, focusing on peer-reviewed studies examining eye movement dysfunction in PD. Relevant publications were identified through PubMed, Scopus, and Web of Science, using key terms, such as “eye movements in Parkinson’s disease”, “saccadic control and neurodegeneration”, “fixation instability in PD”, and “eye-tracking for cognitive assessment”. Studies integrating machine learning (ML) models and VR-based interventions were also included. (4) Results. Patients with PD exhibit distinct saccadic abnormalities, including hypometric saccades, prolonged saccadic latency, and increased anti-saccade errors. These impairments correlate with executive dysfunction and disease progression. Fixation instability and altered pupillary responses further support the role of oculomotor metrics as non-invasive biomarkers. Emerging AI-driven eye-tracking models show promise for automated PD diagnosis and progression tracking. (5) Conclusions. Eye tracking provides a reliable, cost-effective tool for early PD detection, cognitive assessment, and rehabilitation. Future research should focus on standardizing clinical protocols, validating predictive AI models, and integrating eye tracking into multimodal treatment strategies. Full article

Background/Objectives: Genetic factors play an important role in idiopathic rapid eye movement sleep behavior disorder (iRBD) but have not been fully studied. This study aimed to analyze the Parkinson’s disease (PD)-related genetic loci in iRBD in the southern Chinese population. Methods: In this study, we recruited 292 individuals with PD, 62 with iRBD, and 189 healthy controls (HC). Candidate genes were identified primarily from the Parkinson’s Progression Markers Initiative (PPMI) database. Genotypic and allele frequency analyses were conducted to compare the distribution across HC, iRBD, and PD groups. The effects of significant single-nucleotide polymorphisms (SNPs) on gene expression were examined. Clinical manifestations associated with different genotypes were also analyzed. The receiver operating characteristic (ROC) curve and Kaplan–Meier plots were utilized to further verify the diagnostic and predictive value of these SNPs. Results: We identified two significant SNPs associated with iRBD: rs13294100 of SH3GL2 and rs165599 of COMT. Clinical scale and polysomnography data analysis indicated that iRBD patients with the GA or AA genotype at the COMT rs165599 locus have lower RBDSQ scores and higher sleep efficiency. Moreover, we identified that COMT rs165599 and MCCC1 rs12637471 may play an important role in both PD and iRBD, while SNCA rs356181 was different between iRBD and PD. Conclusions: Our research revealed that in the southern Chinese demographic, genetic loci in SH3GL2 and COMT were linked to iRBD and may act as potential biomarkers for iRBD risk. Additionally, there is evidence suggesting a partial genetic overlap between iRBD and PD, indicating a shared genetic predisposition. Full article