Diagnostics最新文献

Background/Objectives: Facial lesions, including lentigo maligna and lentigo maligna melanoma (LM/LMM), both malignant, present significant diagnostic challenges due to their clinical similarity to benign conditions. Although standard dermoscopy is a well-established tool for diagnosis, its inability to reveal cellular-level details highlights the necessity of new magnified techniques. This study aimed to assess the role of standard dermoscopy, high-magnification dermoscopy, and fluorescence-advanced videodermatoscopy (FAV) in diagnosing LM/LMM and differentiating them from benign facial lesions. Methods: This retrospective, observational, multicenter study evaluated 85 patients with facial skin lesions (including LM, LMM, basal-cell carcinoma, solar lentigo, seborrheic keratosis, actinic keratosis, and nevi) who underwent dermatological examination for skin tumor screening. Standard dermoscopy at 30× magnification (D30), high-magnification dermoscopy at 150× magnification (D150), and FAV examination were performed. Dermoscopic images were retrospectively evaluated for the presence of fifteen 30× and twenty-one 150× dermoscopic features, and their frequency was calculated. To compare D30 with D150 and D150 with FAV, the Gwet AC1 concordance index and the correct classification rate (CCR) were estimated. Results: Among 85 facial lesions analyzed, LM/LMM exhibited distinctive dermoscopic features at D30, including a blue-white veil (38.9% vs. 1.7%, p < 0.001), regression structures (55.6% vs. 21.7%, p = 0.013), irregular dots or globules (50.0% vs. 10%, p = 0.001), angulated lines (72.2% vs. 6.7%, p < 0.001), an annular granular pattern (61.1% vs. 20%, p = 0.002), asymmetrical pigmented follicular openings (100.0% vs. 21.7%; p < 0.001), and follicular obliteration (27.8% vs. 3.3%). At D150, roundish melanocytes (87.5% vs. 18.2%, p < 0.001) and melanophages (43.8% vs. 14.5%, p = 0.019) were predominant. FAV examination identified large dendritic cells, isolated melanocytes, and free melanin in LM/LMM (all p < 0.001) with high concordance to D150. Conclusions: Integrating D30, D150, and FAV into clinical practice may enhance diagnostic precision for facial lesions by combining macroscopic and cellular insights, thereby reducing unnecessary biopsies. However, future studies are essential to confirm these results.

Biliary strictures represent a narrowing of the bile ducts, leading to obstruction that may result from benign or malignant etiologies. Accurate diagnosis is crucial but challenging due to overlapping features between benign and malignant strictures. This review presents a comprehensive diagnostic approach that integrates biochemical markers, imaging modalities, and advanced endoscopic techniques to distinguish between these causes. Imaging tools such as ultrasound, MRI/MRCP, and CECT are commonly used, each with distinct advantages and limitations. Furthermore, endoscopic procedures such as ERCP and EUS are key in tissue acquisition, enhancing diagnostic accuracy, especially for indeterminate or complex strictures. Recent innovations, including artificial intelligence and new endoscopic techniques, hold promise in enhancing precision and reducing diagnostic challenges. This review emphasizes a multidisciplinary strategy to improve diagnostic pathways, ensuring timely management for patients with biliary strictures.

Background/Objectives: Dental age estimation is a vital component of forensic science, helping to determine the identity and actual age of an individual. However, its effectiveness is challenged by methodological variability and biological differences between individuals. Therefore, to overcome the drawbacks such as the dependence on manual measurements, requiring a lot of time and effort, and the difficulty of routine clinical application due to large sample sizes, we aimed to automatically estimate tooth age from panoramic radiographs (OPGs) using artificial intelligence (AI) algorithms. Methods: Two-Dimensional Deep Convolutional Neural Network (2D-DCNN) and One-Dimensional Deep Convolutional Neural Network (1D-DCNN) techniques were used to extract features from panoramic radiographs and patient records. To perform age estimation using feature information, Genetic algorithm (GA) and Random Forest algorithm (RF) were modified, combined, and defined as Modified Genetic-Random Forest Algorithm (MG-RF). The performance of the system used in our study was analyzed based on the MSE, MAE, RMSE, and R² values calculated during the implementation of the code. Results: As a result of the applied algorithms, the MSE value was 0.00027, MAE value was 0.0079, RMSE was 0.0888, and R² score was 0.999. Conclusions: The findings of our study indicate that the AI-based system employed herein is an effective tool for age detection. Consequently, we propose that this technology could be utilized in forensic sciences in the future.

Background: COPD is a chronic disease characterized by frequent exacerbations that require hospitalization, significantly increasing the care burden. In recent years, the use of artificial intelligence-based tools to improve the management of patients with COPD has progressed, but the prediction of readmission has been less explored. In fact, in the state of the art, no models specifically designed to make medium-term readmission predictions (2-3 months after admission) have been found. This work presents a new intelligent clinical decision support system to predict the risk of hospital readmission in 90 days in patients with COPD after an episode of acute exacerbation. Methods: The system is structured in two levels: the first one consists of three machine learning algorithms -Random Forest, Naïve Bayes, and Multilayer Perceptron-that operate concurrently to predict the risk of readmission; the second level, an expert system based on a fuzzy inference engine that combines the generated risks, determining the final prediction. The employed database includes more than five hundred patients with demographic, clinical, and social variables. Prior to building the model, the initial dataset was divided into training and test subsets. In order to reduce the high dimensionality of the problem, filter-based feature selection techniques were employed, followed by recursive feature selection supported by the use of the Random Forest algorithm, guaranteeing the usability of the system and its potential integration into the clinical environment. After training the models in the first level, the knowledge base of the expert system was determined on the training data subset using the Wang-Mendel automatic rule generation algorithm. Results: Preliminary results obtained on the test set are promising, with an AUC of approximately 0.8. At the selected cutoff point, a sensitivity of 0.67 and a specificity of 0.75 were achieved. Conclusions: This highlights the system's future potential for the early identification of patients at risk of readmission. For future implementation in clinical practice, an extensive clinical validation process will be required, along with the expansion of the database, which will likely contribute to improving the system's robustness and generalization capacity.

Background: Despite the prevalence and severity of bipolar disorder (BD), current diagnostic approaches remain largely subjective. This study presents an automatic diagnostic framework using electroencephalography (EEG)-derived Hjorth parameters (activity, mobility, and complexity), aiming to establish objective neurophysiological markers for BD detection and provide insights into its underlying neural mechanisms. Methods: Using resting-state eyes-closed EEG data collected from 20 BD patients and 20 healthy controls (HCs), we developed a novel diagnostic approach based on Hjorth parameters extracted across multiple frequency bands. We employed a rigorous leave-one-subject-out cross-validation strategy to ensure robust, subject-independent assessment, combined with explainable artificial intelligence (XAI) to identify the most discriminative neural features. Results: Our approach achieved remarkable classification accuracy (92.05%), with the activity Hjorth parameters from beta and gamma frequency bands emerging as the most discriminative features. XAI analysis revealed that anterior brain regions in these higher frequency bands contributed most significantly to BD detection, providing new insights into the neurophysiological markers of BD. Conclusions: This study demonstrates the exceptional diagnostic utility of Hjorth parameters, particularly in higher frequency ranges and anterior brain regions, for BD detection. Our findings not only establish a promising framework for automated BD diagnosis but also offer valuable insights into the neurophysiological basis of bipolar and related disorders. The robust performance and interpretability of our approach suggest its potential as a clinical tool for objective BD diagnosis.

Background: This study proposes a classification system for predicting chronic obstructive pulmonary disease (COPD) patients and non-patients based on image and text data. Method: This study measured the respiratory volume based on thermal images, stored the respiratory data, and derived features related to respiratory patterns, including the total respiratory volume, average distance between expirations, average distance between inspirations, and total respiratory rate. The data for each feature were stored in text format. The four features saved as text were scaled using Z-score normalization and expressed as scores through weighted summation. These scores were compared to a threshold based on the ROC curve values, classifying participants as patients if the score exceeded the threshold and as non-patients if it fell below. Results: The proposed method achieved an accuracy of 82.5%. To validate the proposed approach, precision, recall, and F1-score were utilized, confirming the high classification performance of the model. The results of this study demonstrate the potential for future applications in non-contact medical examinations and diagnoses of respiratory diseases.

Background/Objectives: Acute pulmonary embolism (APE) is a clinical syndrome characterized by the obstruction of blood flow in the pulmonary artery, whose main pathophysiological features are respiratory and circulatory dysfunction. Acute pulmonary embolism is associated with a high mortality rate. Diagnostic and therapeutic delays can exacerbate mortality and result in prolonged hospitalization. With the increasing understanding that APE is associated with inflammation, various indices based on systemic inflammation have been shown to predict prognosis in patients with APE. The NAPLES Prognostic Score (NPS) is a new scoring system that indicates the inflammatory and nutritional status of the patient based on albumin (ALB) levels, total cholesterol (TC) levels, lymphocyte-to-monocyte ratio (LMR) and neutrophil-to-lymphocyte ratio (NLR). Our study aimed to examinate the effect of NPS on APE prognosis, so the relationship between NPS and APE prognosis was evaluated in our study. In addition, this study seeks to lay the groundwork for further investigations into this association and expand the existing body of knowledge. Methods: The clinical data of patients who applied to the Dicle University Faculty of Medicine and were diagnosed with APE between March 2014 and April 2024 were evaluated retrospectively, with 436 patients aged 18 years and over included in the study. Patients were divided into two groups according to NPS. It was statistically investigated whether there was a significant difference in long-term mortality between the two groups. Statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) version 21.0. Results: Survival was found to be statistically significantly lower in patients with NPS 3-4 (p < 0.05). In the multivariate regression analyses, no statistically significant effect of NPS or other parameters except lactate on 3-month mortality was found (p > 0.05). The short-term prognostic value of the NPS has been found to be equivalent to that of the sPESI score. It may be considered that APE patients with high NPS scores should be monitored more frequently. Conclusions: Increased NPS was found to be associated with poor APE prognosis in our study.

Background: Laeverin is an extravillous trophoblast marker playing a significant role in trophoblast migration. We endeavored to estimate the association between the amniotic and serum laeverin concentrations at 16-22 weeks of gestation and the fetal and placental ultrasound measurements in high-risk uncomplicated pregnancies. Methods: A prospective cross-sectional study of consecutively recruited singleton pregnancies undergoing amniocentesis was performed. Fetal structural malformations and/or aneuploidy were the exclusion criteria. Fetal biometric parameters and placental growth/perfusion were assessed by ultrasound in 44 high-risk pregnancies who had no pregnancy complications and any other chronic disease. Maternal serum and amniotic laeverin levels were essayed with sandwich enzyme-linked immunosorbent assay. Results: Serum laeverin levels are decreasing marginally with the maternal age in mid-gestation. Laeverin levels in the serum correlated minimally negatively with head size of the fetus (β = -0.38; p < 0.05; 95% confidence interval (CI) -0.03-0.01), whereas the amniotic level correlated strongly with the fetal abdominal circumference (β = -0.74; p < 0.05; 95% CI: -0.34--0.09). In addition, the amniotic laeverin level correlated moderately and positively with the placental volume (β = 0.46; p < 0.05; 95% CI: 0.01-0.08). Conclusions: Laeverin levels detected in the serum and in the amniotic fluid denote the fetoplacental growth in uncomplicated high-risk pregnancies.

Childhood glaucoma encompasses a group of rare but severe ocular disorders characterized by increased intraocular pressure (IOP), posing significant risks to vision and quality of life. Primary congenital glaucoma has a prevalence of one in 10,000-68,000 people in Western countries. More worryingly, it is responsible for 5-18% of all childhood blindness cases. According to the Childhood Glaucoma Research Network (CGRN), this spectrum of disease is classified into primary glaucoma (primary congenital glaucoma and juvenile open-angle glaucoma) and secondary glaucomas (associated with non-acquired ocular anomalies, non-acquired systemic disease, acquired conditions, and glaucoma after cataract surgery). They present very specific ocular characteristics, such as buphthalmos or progressive myopic shift, corneal modifications such as Haab striae, corneal edema or increased corneal diameter, and also glaucoma findings including high intraocular pressure, specific visual fields abnormalities, and optic nerve damage such as increased cup-disc ratio, cup-disc ratio asymmetry of at least 0.2 and focal rim thinning. Surgical intervention remains the cornerstone of treatment, and initial surgical options include angle surgeries such as goniotomy and trabeculotomy, aimed at improving aqueous outflow. For refractory cases, trabeculectomy and glaucoma drainage devices (GDDs) serve as second-line therapies. Advanced cases may require cyclodestructive procedures, including transscleral cyclophotocoagulation, reserved for eyes with limited visual potential. All in all, with appropriate management, the prognosis of PCG may be quite favorable: stationary disease has been reported in 90.3% of cases after one year, with a median visual acuity in the better eye of 20/30. Immediate recognition of the specific signs and symptoms by caregivers, primary care providers, and ophthalmologists, followed by prompt diagnosis, comprehensive surgical planning, and involving the caregivers in the follow-up schedule remain critical for optimizing outcomes in childhood glaucoma management.

Objective: Sepsis is a life-threatening response to infection and a major cause of hospital mortality. Machine learning (ML) models have demonstrated better sepsis prediction performance than integer risk scores but are less widely used in clinical settings, in part due to lower interpretability. This study aimed to improve the interpretability of an ML-based model without reducing its performance in non-ICU sepsis prediction. Methods: A logistic regression model was trained to predict sepsis onset and then converted into a more interpretable integer point system, STEWS, using its regression coefficients. We compared STEWS with the logistic regression model using PPV at 90% sensitivity. Results: STEWS was significantly equivalent to logistic regression using the two one-sided tests procedure (0.051 vs. 0.051; p = 0.004). Conclusions: STEWS demonstrated equivalent performance to a comparable logistic regression model for non-ICU sepsis prediction, suggesting that converting ML models into more interpretable forms does not necessarily reduce predictive power.