Diagnostics最新文献

{"title":"Challenges in the Pediatric Celiac Disease Diagnosis: An Up-to-Date Review.","authors":"Alexandra Mpakosi, Christiana Kaliouli-Antonopoulou, Vasileios Cholevas, Stamatios Cholevas, Ioannis Tzouvelekis, Maria Mironidou-Tzouveleki, Alexandra Lianou, Nicoletta Iacovidou, Andreas G Tsantes, Rozeta Sokou","doi":"10.3390/diagnostics15182392","DOIUrl":"10.3390/diagnostics15182392","url":null,"abstract":"<p><p>Celiac disease (CD) is an autoimmune disorder that affects genetically susceptible individuals, characterized by specific serological and histological features, and is triggered by the consumption of gluten. The current diagnosis is based on the demonstration of intestinal damage in small bowel biopsies, as well as the serological presence of CD-specific antibodies (usually IgA) against tissue transglutaminase (tTG), deamidated gliadin peptides (DGP), and endomysium (EMA). The European Society for Paediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN), in the 2020 updated guidelines, states that the diagnosis of CD in children and adolescents can be established without a biopsy if they have IgA tTG2 >10 ULN confirmed by positive IgA endomysial antibodies on two separate blood tests. Challenges, though, arise in serological and clinical diagnosis: in several cases false-positive results are observed. False-negative serological tests may also occur in children < 2 years of age, in patients adhering to a gluten-free diet, in individuals on immunosuppressive therapy, in cases of selective IgA deficiency, and finally due to potential laboratory errors. CD has a wide range of clinical manifestations, either gastrointestinal or extraintestinal. However, CD may be clinically silent and diagnosed through screening. Delayed diagnosis and treatment can lead to serious complications. Therefore, understanding and awareness of these challenges is imperative. Hence, the aim of this review is to highlight the diagnostic challenges of celiac disease in children and adolescents and stress the importance of prompt recognition in order to ensure appropriate management and prevention of complications.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 18","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468248/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Colorectal Cancer-One Disease, Two Fires: Distinct Inflammatory Landscapes in Colon and Rectal Cancer.","authors":"Catalin Vladut Ionut Feier, Florin Grama, Georgiana Viorica Moise, Razvan Constantin Vonica, Vasile Gaborean, Alaviana Monique Faur, Vladut Iosif Rus, Calin Muntean","doi":"10.3390/diagnostics15182387","DOIUrl":"10.3390/diagnostics15182387","url":null,"abstract":"<p><p><b>Background:</b> Systemic inflammatory indices are increasingly used to predict prognosis in colorectal cancer (CRC), yet direct comparisons between colon cancer (CC) and rectal cancer (RC) remain limited. <b>Methods:</b> We conducted a retrospective matched-cohort study including 296 patients (148 with CC and 148 with RC) surgically treated between January 2018 and December 2024. Patients were matched by tumor stage, sex, and age (±3 years). Preoperative blood samples were used to calculate several inflammatory markers, including Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Monocyte-to-Lymphocyte Ratio (MLR), Systemic Inflammation Response Index (SIRI), Systemic Immune-Inflammation Index (SII), and Aggregate Index of Systemic Inflammation (AISI). Subgroup analyses were performed based on the Charlson Comorbidity Index (>3 vs. ≤3), surgical context (elective vs. emergency), and tumor stage (T1-T2 vs. T3-T4). <b>Results:</b> Colon cancer patients exhibited significantly higher levels of systemic inflammation compared to those with rectal cancer, with notable differences in NLR (3.99 vs. 2.84, <i>p</i> < 0.001), PLR (219.8 vs. 163.3, <i>p</i> < 0.001), SIRI (3.7 vs. 1.91, <i>p</i> = 0.004), SII (1533.8 vs. 847.8, <i>p</i> < 0.001), and AISI (1714.7 vs. 593.6, <i>p</i> = 0.009). These differences remained statistically significant in key subgroups. In elective surgeries, CC patients had elevated PLR (<i>p</i> < 0.001), SIRI (<i>p</i> = 0.003), SII (<i>p</i> < 0.001), and AISI (<i>p</i> = 0.013). Among patients with advanced tumors (T3-T4), CC was associated with higher SII (<i>p</i> < 0.001), AISI (<i>p</i> = 0.008), PLR (<i>p</i> < 0.001), and SIRI (<i>p</i> = 0.004). For those with a Charlson index > 3, CC patients showed significantly higher PLR (<i>p</i> < 0.001), NLR (<i>p</i> < 0.001) and SIRI (<i>p</i> = 0.001). <b>Conclusions:</b> colon cancer presents with a markedly stronger systemic inflammatory response than rectal cancer, particularly in patients with advanced disease, elective surgical treatment, and higher comorbidity burden. These findings suggest that indices such as SIRI, SII, and PLR may serve as valuable stratification tools beyond tumor location in CRC.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 18","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468290/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Applications of Anterior Segment Optical Coherence Tomography in Managing Phakic and Secondary Intraocular Lens Implants: A Comprehensive Review.","authors":"José Ignacio Fernández-Vigo, Bárbara Burgos-Blasco, Lucía De-Pablo-Gómez-de-Liaño, Ignacio Almorín-Fernández-Vigo, Pedro Arriola-Villalobos, Diego Ruiz-Casas, Ana Macarro-Merino, José Ángel Fernández-Vigo","doi":"10.3390/diagnostics15182385","DOIUrl":"10.3390/diagnostics15182385","url":null,"abstract":"<p><p>Anterior segment optical coherence tomography (AS-OCT) has emerged as a crucial imaging technique in ophthalmology, particularly for evaluating intraocular structures and the behavior of phakic and secondary intraocular lenses (IOLs). This narrative review summarizes the latest findings and clinical applications of OCT regarding phakic and secondary IOLs, focusing on their effectiveness, safety, and factors influencing performance. Through a comprehensive analysis of current literature, we explore how OCT facilitates the assessment of IOLs on key anatomical parameters-such as vault, angle configuration, lens centration, tilt, and haptic positioning-essential for optimizing surgical outcomes and minimizing postoperative complications. In phakic IOLs, including posterior chamber lenses such as the Implantable Collamer Lens (ICL, STAAR Surgical, Monrovia, CA, USA) and iris-fixated lenses, such as Artiflex (Ophtec BV, Groningen, The Netherlands), OCT enables precise evaluation of the anterior segment, aiding both candidate selection and long-term monitoring. In secondary implants for aphakia-especially iris-fixated lenses like Artisan (Ophtec BV, Groningen, The Netherlands) and sutureless scleral-fixated lenses such as the Carlevale IOL (Soleko, Rome, Italy)-or those implanted via the Yamane technique, OCT provides high-resolution visualization of haptic fixation, IOL stability, and potential complications, including tilt or decentration. This review also highlights comparative insights between fixation techniques, underscores the need for standardized OCT protocols, and discusses the integration of artificial intelligence tools. In summary, the routine use of OCT in the preoperative and postoperative management of phakic and secondary IOLs has been increasingly incorporated into clinical practice, as it enhances clinical decision-making and improves patient outcomes.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 18","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468152/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anatomy-Guided Microsurgical Resection of a Dominant Frontal Lobe Tumor Without Intraoperative Adjuncts: A Case Report from a Resource-Limited Context.","authors":"Matei Șerban, Corneliu Toader, Răzvan-Adrian Covache-Busuioc","doi":"10.3390/diagnostics15182393","DOIUrl":"10.3390/diagnostics15182393","url":null,"abstract":"<p><p><b>Background:</b> Glioblastoma (GBM), IDH-wildtype, is one of the most aggressive primary brain malignancies, and maximal safe resection is consistently recognized as a significant prognostic factor. Intraoperative adjuncts including functional mapping, neuronavigation, and fluorescence-guidance are not always present in many centers around the world. The aim is not to suggest equivalence to adjunct-assisted resections, but rather to illustrate the feasibility of anatomy-guided surgery in carefully selected cases and to contribute to the broader discussion on safe operative strategies in resource-limited environments. <b>Methods</b>: We present the case of a 54-year-old right-handed male who presented with progressive non-fluent aphasia, seizures, and signs of intracranial hypertension. Pre-operative MRI showed a heterogeneously hyperintense, frontobasal intra-axial mass involving the dominant inferior frontal gyrus, extending toward the corpus callosum and orbitofrontal cortex, and early subfalcine shift. Surgery was performed via a left frontobasal craniotomy, using subpial dissection and cortical-sulcal anatomical landmarks while aiming to preserve eloquent subcortical tracts (frontal aslant tract, superior longitudinal fasciculus). Nueronavigation, functional mapping or fluorescence was not used. We defined our outcomes by the extent of resection, functional preservation, and early radiological stability. <b>Results</b>: The procedure achieved a subtotal-near-total resection (>95% estimated volume) while maintaining functional motor function from prior to surgery and the patient's baseline expressive aphasia, with no new neurological deficits. Early post-operative CT showed decompression of the resection cavity without hemorrhage or shift. At three months post-operative, CT showed stability of the cavity and resolution of the most perilesional edema with no evidence of recurrence. Clinically, the patient showed gradual improvement in verbal fluency, he remained seizure free, and maintained independence, which allowed for timeliness of the initiation of adjuvant chemoradiotherapy. <b>Conclusions</b>: We intend for the case to illustrate that, in selected dominant frontal GBM, following microsurgical anatomical principles closely may provide a high extent of resection with the preservation of function, even without advanced intraoperative adjuncts. We hope that our experience may support our colleagues who practice in resource-limited settings and contribute to our shared goal of both oncological outcomes and the quality of life of our patients.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 18","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dental Age Estimation from Panoramic Radiographs: A Comparison of Orthodontist and ChatGPT-4 Evaluations Using the London Atlas, Nolla, and Haavikko Methods.","authors":"Derya Dursun, Rumeysa Bilici Geçer","doi":"10.3390/diagnostics15182389","DOIUrl":"10.3390/diagnostics15182389","url":null,"abstract":"<p><p><b>Background:</b> Dental age (DA) estimation, which is widely used in orthodontics, pediatric dentistry, and forensic dentistry, predicts chronological age (CA) by assessing tooth development and maturation. Most methods rely on radiographic evaluation of tooth mineralization and eruption stages to assess DA. With the increasing adoption of large language models (LLMs) in medical sciences, use of ChatGPT has extended to processing visual data. The aim of this study, therefore, was to evaluate the performance of ChatGPT-4 in estimating DA from panoramic radiographs using three conventional methods (Nolla, Haavikko, and London Atlas) and to compare its accuracy against both orthodontist assessments and CA. <b>Methods:</b> In this retrospective study, panoramic radiographs of 511 Turkish children aged 6-17 years were assessed. DA was estimated using the Nolla, Haavikko, and London Atlas methods by both orthodontists and ChatGPT-4. The DA-CA difference and mean absolute error (MAE) were calculated, and statistical comparisons were performed to assess accuracy and sex differences and reach an agreement between the evaluators, with significance set at <i>p</i> < 0.05. <b>Results:</b> The mean CA of the study population was 12.37 ± 2.95 years (boys: 12.39 ± 2.94; girls: 12.35 ± 2.96). Using the London Atlas method, the orthodontists overestimated CA with a DA-CA difference of 0.78 ± 1.26 years (<i>p</i> < 0.001), whereas ChatGPT-4 showed no significant DA-CA difference (0.03 ± 0.93; <i>p</i> = 0.399). Using the Nolla method, the orthodontist showed no significant DA-CA difference (0.03 ± 1.14; <i>p</i> = 0.606), but ChatGPT-4 underestimated CA with a DA-CA difference of -0.40 ± 1.96 years (<i>p</i> < 0.001). Using the Haavikko method, the evaluators underestimated CA (orthodontist: -0.88; ChatGPT-4: -1.18; <i>p</i> < 0.001). The lowest MAE for ChatGPT-4 was obtained when using the London Atlas method (0.59 ± 0.72), followed by Nolla (1.33 ± 1.28) and Haavikko (1.51 ± 1.41). For the orthodontists, the lowest MAE was achieved when using the Nolla method (0.86 ± 0.75). Agreement between the orthodontists and ChatGPT-4 was highest when using the London Atlas method (ICC = 0.944, r = 0.905). <b>Conclusions:</b> ChatGPT-4 showed the highest accuracy with the London Atlas method, with no significant difference from CA for either sex or the lowest prediction error. When using the Nolla and Haavikko methods, both ChatGPT-4 and the orthodontist tended to underestimate age, with higher errors. Overall, ChatGPT-4 performed best when using visually guided methods and was less accurate when using multi-stage scoring methods.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 18","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Kinetics of High-Sensitive Cardiac Troponin I in Patients with ST-Segment Elevation Myocardial Infarction and Non-ST Segment Elevation Myocardial Infarction.","authors":"Adi Haizler, Ranel Loutati, Louay Taha, Mohammad Karmi, Dana Deeb, Mohammed Manassra, Noam Fink, Pierre Sabouret, Jamal S Rana, Mamas A Mamas, Ofir Rabi, Akiva Brin, Amro Moatz, Maayan Shrem, Abed Qadan, Nir Levi, Michael Glikson, Elad Asher, On Behalf Of The Jerusalem Platelets Thrombosis And Intervention In Cardiology Jupiter-Study Group","doi":"10.3390/diagnostics15182390","DOIUrl":"10.3390/diagnostics15182390","url":null,"abstract":"<p><p><b>Background/Objectives:</b> Existing data regarding the kinetics of cardiac troponin I (cTnI) are limited. The aim of the current study was to evaluate the kinetics of highly sensitive (hs) cTnI following acute myocardial infarction (MI) in a large-scale, real-world cohort. <b>Methods:</b> A prospective observational cohort study included all consecutive patients admitted to the intensive cardiovascular care unit (ICCU) with ST-segment elevation MI (STEMI) and non-ST-segment elevation MI (NSTEMI) who underwent percutaneous coronary intervention (PCI) between January 2020 and April 2024. Hs-cTnI concentrations were measured at the time of presentation and daily thereafter. <b>Results:</b> A total of 1174 STEMI patients [191 females (16.3%)] with a mean age of 63 years and 767 NSTEMI patients [137 females (17.9%)] with a mean age of 66.7 years were enrolled. The average hs-cTnI peak levels were 77,937.99 ng/L and 24,804.73 ng/L for STEMI and NSTEMI patients, respectively. A single peak of hs-cTnI was observed in 83% and 78% of STEMI and NSTEMI patients, respectively, while two peaks were observed in 11% and 19% and three or more peaks were observed in 6% and 3% of STEMI and NSTEMI patients, respectively. A higher number of peaks was associated with a lower ejection fraction and more in-hospital complications. Additionally, a higher number of peaks correlated with a higher in-hospital mortality rate among NSTEMI patients. <b>Conclusions:</b> Most STEMI and NSTEMI patients displayed a monophasic kinetic pattern of hs-cTnI peak levels. However, a greater number of hs-cTnI peaks was linked to a higher incidence of clinical complications, lower ejection fraction, and increased mortality.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 18","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electroencephalography-Based Machine Learning for Biomarker Detection in Dyslexia and Autism Spectrum Disorder: A Comparative Review of Models, Features, and Diagnostic Utility.","authors":"Günet Eroğlu","doi":"10.3390/diagnostics15182388","DOIUrl":"10.3390/diagnostics15182388","url":null,"abstract":"<p><p>To uncover neurobiological indicators related to autism spectrum disorders and developmental dyslexia, this article gives a full overview of the most recent advances in machine learning and deep learning methods based on electroencephalography. We look into methodological pipelines that include signal gathering, preprocessing, feature engineering, model selection, and interpretability procedures. We based these pipelines on 15 peer-reviewed research papers published between 2013 and 2025. Most of the research employed the 10-20 system for resting-state EEG and followed MATLAB, MNE-Python, or EEGLAB guidelines for preprocessing. The feature sets included spectral power, functional connectivity, task-evoked potentials, and entropy measures. People used many standard ML methods, such as support vector machines and random forests, as well as more advanced models, like deep neural networks and transformer-based architectures. Several studies found that both dyslexic and ASD groups did well at classifying, with accuracy scores between 82% and 99.2%. The new models could be used in therapeutic settings, but there are still problems with how easy they are to understand and how well they apply to a wide range of situations. This is especially true for ASD because its spectrum is so varied.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 18","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468693/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Artificial Intelligence to Develop Clinical Decision Support Systems-The Evolving Road of Personalized Oncologic Therapy.","authors":"Elena Chitoran, Vlad Rotaru, Aisa Gelal, Sinziana-Octavia Ionescu, Giuseppe Gullo, Daniela-Cristina Stefan, Laurentiu Simion","doi":"10.3390/diagnostics15182391","DOIUrl":"10.3390/diagnostics15182391","url":null,"abstract":"<p><p><b>Background/Objectives:</b> The use of artificial intelligence (AI) in oncology has the potential to improve decision making, particularly in managing the risk associated with targeted therapies. This study aimed to develop and validate a machine learning-based clinical decision support system (CDSS) capable of predicting complications associated with Bevacizumab or its biosimilars and to translate the resulting predictive model into a clinically applicable tool. <b>Methods:</b> A prospective observational study was conducted on 395 records from patients treated with Bevacizumab or biosimilars for solid tumors. Pretherapeutic variables, such as demographic data, medical history, tumor characteristics and laboratory findings, were retrieved from medical records. Several machine learning models (logistic regression, Random Forest, XGBoost) were trained using 70/30 and 80/20 data splits. Their predictive performances were compared using accuracy, AUC-ROC, sensitivity, specificity, F1-scores and error rate. The best-performing model was used to derive a logistic-based risk score, which was further implemented as an interactive HTML form. <b>Results:</b> The optimized Random Forest model trained on the 80/20 split demonstrated the best balance between accuracy (70.63%), sensitivity (66.67%), specificity (73.85%), and AUC-ROC (0.75). The derived logistic risk score showed good performance (AUC-ROC = 0.720) and calibration. It identified variables, such as age ≥ 65, anemia, elevated urea, leukocytosis, tumor differentiation, and stage, as significant predictors of complications. The final tool provides clinicians with an easy-to-use, offline form that estimates individual risk levels and stratifies patients into low-, intermediate-, or high-risk categories. <b>Conclusions:</b> This study offers a proof of concept for developing AI-supported predictive tools in oncology using real-world data. The resulting logistic risk score and interactive form can assist clinicians in tailoring therapeutic decisions for patients receiving targeted therapies, enhancing the personalization of care without replacing clinical judgment.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 18","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468058/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RETRACTED: Hasanin et al. Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm. <i>Diagnostics</i> 2022, <i>12</i>, 2844.","authors":"Tawfiq Hasanin, Pravin R Kshirsagar, Hariprasath Manoharan, Sandeep Singh Sengar, Shitharth Selvarajan, Suresh Chandra Satapathy","doi":"10.3390/diagnostics15182384","DOIUrl":"10.3390/diagnostics15182384","url":null,"abstract":"<p><p>The journal retracts the article titled \"Exploration of Despair Eccentricities Based on Scale Metrics with Feature Sampling Using a Deep Learning Algorithm\" [...].</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 18","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12468785/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Macular Choroidal Thickness in Keratoconus: Systematic Review and Meta-Analysis of Current Evidence.","authors":"Dimitrios Kazantzis, Genovefa Machairoudia, Panagiotis Theodossiadis, Irini Chatziralli","doi":"10.3390/diagnostics15182394","DOIUrl":"10.3390/diagnostics15182394","url":null,"abstract":"<p><p><b>Background/Objectives</b>: This study aimed to investigate changes and synthesize the existing evidence in macular choroidal thickness in patients with keratoconus (KC) compared to healthy controls, utilizing optical coherence tomography (OCT). <b>Methods</b>: PubMed and Scopus databases were systematically searched for published articles comparing choroidal thickness between patients with KC and healthy controls. The Mean Difference (MD) with 95% confidence interval (CI) was computed to compare continuous variables. Our study was registered with PROSPERO with registration ID: CRD42024605227. Revman 5.4 was used for the analysis. <b>Results</b> 10 studies were included in the analysis. Subfoveal choroidal thickness was increased in patients with KC compared to controls. (MD = 43.94, 95% CI = 17.36-70.51, <i>p</i> = 0.001, I² = 95%). Leave-one-out sensitivity analysis confirmed this finding. <b>Conclusions</b>: Our meta-analysis demonstrated that eyes with keratoconus have significantly increased macular choroidal thickness compared to controls. These findings highlight the need for longitudinal studies stratified by disease severity to clarify the role of choroidal changes in keratoconus progression.</p>","PeriodicalId":11225,"journal":{"name":"Diagnostics","volume":"15 18","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12469044/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145173912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}