Diagnostics最新文献

Background: Machine learning offers new options for glucose prediction and real-time glucose management. The aim of this study was to develop a machine learning-based algorithm that takes into account glucose dynamics patterns for predicting nocturnal glucose in individuals with type 1 diabetes. Methods: To identify glucose patterns, we applied a hierarchical clustering algorithm to real-time continuous glucose monitoring data obtained from 570 adult patients. Machine learning algorithms with or without pre-clustering were used for modeling. Results: Eight clusters without nocturnal hypoglycemia and six clusters with at least one low-glucose episode were identified by the cluster analysis. When forecasting time series without hypoglycemia with a prediction horizon (PH) of 15 or 30 min, gradient boosting trees (GBTs) with pre-clustering and random forest (RF) with pre-clustering outperformed algorithms based on medoids of time series clusters, the Holt model, and GBTs without pre-clustering. When forecasting time series with low-glucose episodes, a model based on the pre-clustering and GBTs provided the highest predictive accuracy at PH = 15 min, and a model based on RF with pre-clustering was the best at PH = 30 min. Conclusions: The results indicate that the clustering of glucose dynamics can enhance the efficacy of machine learning algorithms used for glucose prediction.

Background/objectives: The Strengths and Difficulties Questionnaire (SDQ) is a widely used 25-item screening and diagnostic tool for behavioral and emotional problems in young people. Despite its popularity, evaluations of the SDQ's factor structure in adolescent populations have produced disparate results, and its relationships with theoretically related variables are rarely evaluated. In the present study, these two elements of validity were evaluated based on a large sample of Western Australian adolescents.

Methods: Participants were 1489 adolescents, n = 623 males with a mean age of 13.79 years (SD = 1.61) and n = 866 females, with a mean age of 14.29 years (SD = 1.51). Participants completed the SDQ alongside measures of loneliness, sense of belonging, depression, bullying, and diagnostic status to evaluate its internal structure and correlations with theoretically related variables.

Results: Confirmatory factor analyses supported the internal structure of the SDQ both for males and for females. Relationships between the SDQ subscale scores and those from theoretically related variables were also aligned with the instrument's underpinning framework.

Conclusions: Despite the somewhat disparate results of previous studies, overall, this study supported the validity of the SDQ for use in the Western Australian context.

Background: Pain in the sacroiliac joint is the most prevalent and often overlooked. The sacroiliac joints are thought to be sources of pain in roughly 10% to 25% of patients with chronic lower back pain. Due to the biomechanical nature of the joint, muscle imbalance is the most important cause of sacroiliac joint dysfunction. The hamstring and gluteus medius are the primary muscles involved in postural dysfunction-related muscle imbalance; however, the quadratus lumborum's role in the compensatory mechanism is becoming more apparent, and its potential for treatment in conjunction with gluteus medius strengthening has not yet been investigated. Gluteus medius exercises, along with conventional treatment, are routinely given to patients with sacroiliac joint dysfunction; however, the aim of this study is to explore the additional effects of the muscle energy technique (MET) on the quadratus lumborum along with strengthening of the gluteus medius on pain, disability and quality of life of patients with sacroiliac joint dysfunction.

Methods: Using a computer-generated random number table, seventy patients with unilateral sacroiliac joint pain were divided equally and randomly into two groups. Prior to initiating treatment, baseline measurements were taken using a hand-held dynamometer, visual analog scale (VAS), Oswestry Disability Index (ODI-U) and short form 36-item survey (SF-36v2) to assess strength, pain, functional disability and quality of life, respectively. Over the course of four weeks, all patients received twelve sessions, and both the pre- and post-intervention outcome measures were documented.

Results: After 4 weeks of treatment, both groups showed statistically significant (p < 0.005) mean improvements in muscle strength, pain, disability and quality of life before and after intervention. However, the mean improvements in post-intervention on a dynamometer, VAS, ODI and SF-36 were better in the MET with exercise group (METGME) as compared to the conventional group with exercise (CTGME), with a larger effect size.

Conclusions: The muscle energy technique, applied to the quadratus lumborum in combination with gluteus medius strengthening, is more effective clinically and significantly in improving pain, disability and quality of life in comparison to conventional treatment of sacroiliac joints with gluteus medius exercises.

Objectives: Subarachnoid Hemorrhage (SAH) is a serious neurological emergency case with a higher mortality rate. An automatic SAH detection is needed to expedite and improve identification, aiding timely and efficient treatment pathways. The existence of noisy and dissimilar anatomical structures in NCCT images, limited availability of labeled SAH data, and ineffective training causes the issues of irrelevant features, overfitting, and vanishing gradient issues that make SAH detection a challenging task. Methods: In this work, the water waves dynamic factor and wandering strategy-based Sand Cat Swarm Optimization, namely DWSCSO, are proposed to ensure optimum feature selection while a Parametric Rectified Linear Unit with a Stacked Convolutional Neural Network, referred to as PRSCNN, is developed for classifying grades of SAH. The DWSCSO and PRSCNN surpass current practices in SAH detection by improving feature selection and classification accuracy. DWSCSO is proposed to ensure optimum feature selection, avoiding local optima issues with higher exploration capacity and avoiding the issue of overfitting in classification. Firstly, in this work, a modified region-growing method was employed on the patient Non-Contrast Computed Tomography (NCCT) images to segment the regions affected by SAH. From the segmented regions, the wide range of patterns and irregularities, fine-grained textures and details, and complex and abstract features were extracted from pre-trained models like GoogleNet, Visual Geometry Group (VGG)-16, and ResNet50. Next, the PRSCNN was developed for classifying grades of SAH which helped to avoid the vanishing gradient issue. Results: The DWSCSO-PRSCNN obtained a maximum accuracy of 99.48%, which is significant compared with other models. The DWSCSO-PRSCNN provides an improved accuracy of 99.62% in CT dataset compared with the DL-ICH and GoogLeNet + (GLCM and LBP), ResNet-50 + (GLCM and LBP), and AlexNet + (GLCM and LBP), which confirms that DWSCSO-PRSCNN effectively reduces false positives and false negatives. Conclusions: the complexity of DWSCSO-PRSCNN was acceptable in this research, for while simpler approaches appeared preferable, they failed to address problems like overfitting and vanishing gradients. Accordingly, the DWSCSO for optimized feature selection and PRSCNN for robust classification were essential for handling these challenges and enhancing the detection in different clinical settings.

Background/objectives: Sporadic Creutzfeldt-Jakob disease (sCJD) is a fatal neurodegenerative disorder traditionally diagnosed based on the World Health Organization (WHO) criteria in 1998. Recently, Hermann et al. proposed updated diagnostic criteria incorporating advanced biomarkers to enhance early detection of sCJD. This study aimed to evaluate the sensitivity and specificity of Hermann's criteria compared with those of the WHO criteria in a large cohort of patients suspected of prion disease in Japan.

Methods: In this retrospective cohort study, we examined the new criteria using data of 2004 patients with suspected prion disease registered with the Japanese Prion Disease Surveillance (JPDS) between January 2009 and May 2023. Patients with genetic or acquired prion diseases or incomplete data necessary for the diagnostic criteria were excluded, resulting in 786 eligible cases. The sensitivity and specificity of the WHO and Hermann's criteria were calculated by comparing diagnoses with those made by the JPDS Committee.

Results: Of the 786 included cases, Hermann's criteria helped identify 572 probable cases compared with 448 by the WHO criteria. The sensitivity and specificity of the WHO criteria were 96.4% and 96.6%, respectively. Hermann's criteria demonstrated a sensitivity of 99.3% and a specificity of 95.2%, indicating higher sensitivity but slightly lower specificity. Fifty-five cases were classified as "definite" by both criteria.

Conclusions: The findings suggest that Hermann's criteria could offer improved sensitivity for detecting sCJD, potentially reducing diagnostic oversight. However, caution is advised in clinical practice to avoid misdiagnosis, particularly in treatable neurological diseases, by ensuring thorough exclusion of other potential conditions.

Background/Objectives: Hwa-byung (HB), also known as "anger syndrome" or "fire illness", is a culture-bound syndrome primarily observed among Koreans. This study aims to develop a short-form version of the HB symptom scale using machine learning approaches. Methods: Utilizing exploratory factor analysis (EFA) and various machine learning techniques (i.e., XGBoost, Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, and Multi-Layer Perceptron), we sought to create an efficient HB assessment tool. A survey was conducted on 500 Korean adults using the original 15-item HB symptom scale. Results: The EFA revealed two distinct factors: psychological symptoms and somatic manifestations of HB. Statistical testing showed no significant differences between using different numbers of items per factor (ANOVA: F = 0.8593, p = 0.5051), supporting a minimalist approach with one item per factor. The resulting two-item short-form scale (Q3 and Q10) demonstrated high predictive power for the presence of HB. Multiple machine learning models achieved a consistent accuracy (90.00% for most models) with high discriminative ability (AUC = 0.9436-0.9579), with the Multi-Layer Perceptron showing the highest performance (AUC = 0.9579). The models showed balanced performance in identifying both HB and non-HB cases, with precision and recall values consistently around 0.90. Conclusions: The findings of this study highlighted the effectiveness of integrating EFA and artificial intelligence via machine learning in developing practical assessment tools. This study contributes to advancing methodological approaches for scale development and offers a model for creating efficient assessments of Korean medicine.

Objective: Scaphoid fractures, particularly occult and non-displaced fractures, are difficult to detect using traditional X-ray methods because of their subtle appearance and variability in bone density. This study proposes a two-stage CNN approach to detect and classify scaphoid fractures using anterior-posterior (AP) and lateral (LA) X-ray views for more accurate diagnosis.

Methods: This study emphasizes the use of multi-view X-ray images (AP and LA views) to improve fracture detection and classification. The multi-view fusion module helps integrate information from both views to enhance detection accuracy, particularly for occult fractures that may not be visible in a single view. The proposed method includes two stages, which are stage 1: detect the scaphoid bone using Faster RCNN and a Feature Pyramid Network (FPN) for region proposal and small object detection. The detection accuracy for scaphoid localization is 100%, with Intersection over Union (IoU) scores of 0.8662 for AP views and 0.8478 for LA views. And stage 2: perform fracture classification using a ResNet backbone and FPN combined with a multi-view fusion module to combine features from both AP and LA views. This stage achieves a classification accuracy of 89.94%, recall of 87.33%, and precision of 90.36%.

Results: The proposed model performs well in both scaphoid bone detection and fracture classification. The multi-view fusion approach significantly improves recall and accuracy in detecting fractures compared to single-view approaches. In scaphoid detection, both AP and LA views achieved 100% detection accuracy. In fracture detection, using multi-view fusion, the accuracy for AP views reached 87.16%, and for LA views, it reached 83.83%.

Conclusions: The multi-view fusion model effectively improves the detection of scaphoid fractures, particularly in cases of occult and non-displaced fractures. The model provides a reliable, automated approach to assist clinicians in detecting and diagnosing scaphoid fractures more efficiently.

Background/Objectives: This study's aim was to compare the precision, agreement, and accuracy in axial length (AL) measurements of Argos^® (Alcon Healthcare, US) and Eyestar 900^® (Haag-Streit, Switzerland) swept-source optical coherence tomography (SS-OCT) biometers. Methods: We performed a prospective evaluation of two diagnostic devices. Three consecutive measurements of AL with the Argos^® and the Eyestar^® 900 SS-OCT biometers were conducted in random order in eyes undergoing cataract surgery in Barcelona, Spain. The main endpoint was the median difference in AL between devices. Secondary endpoints included agreement on Bland-Altman plots and 95% limits of agreement (LoAs), repeatability as measured within-subject standard deviation (S_W), percent of failed AL measurements, percent of eyes within ±0.50 D and ±1.00 D one month after surgery, and median and mean prediction error. Results: We included 107 eyes of 107 patients (60.8% females, mean age of 73.1 years). The median difference in AL (Argos^®-Eyestar 900^®) was -0.01 mm (interquartile range [IQR], 0.06), p = 0.01. The 95% LoAs were -0.11 to +0.08 mm, with a trend towards less extreme measurements with Argos^® for very short and long eyes. The median (IQR) Sw was 0.0058 (0.0058) and 0.0000 (0.0058) for Argos^® and Eyestar 900^®, respectively. There were no failed AL measurements with either device (0%, 95% CI = 0% to 3.4%). Overall, 96.1% of eyes were within ±0.50 D and 100% were within ±1.00 D. Conclusions: Argos^® and Eyestar 900^® provided statistically different but clinically negligible differences in AL. However, they are not interchangeable in very long or short eyes, due to the different principles used to determine AL.

(1) Background: Shoulder pathologies are mostly found in overhead sports. Many risk factors have been identified, in particular a deficit in the kinetic chain. The aim of this review was to find out whether prevention by strengthening the kinetic chain can have an impact on the rate of shoulder injury in overhead pitching athletes. (2) Methods: A systematic review of the literature was carried out, including studies on the role of the kinetic chain in the prevention of overhead athletes. The studies used were works published over the last 10 years searched on PubMed, Cochrane Library, PEDro and Science Direct. They were also analyzed by methodological quality scales: the PEDro scale and the Newcastle-Ottawa scale. (3) Results: Eight studies met the inclusion criteria. The studies analyzed revealed a significant correlation between the use of the kinetic chain and the prevention of shoulder injuries, associating factors such as muscle strength, physical performance in tests (CMJ, FMS), static and dynamic balance and the ability to transfer energy from the lower to the upper body. (4) Conclusions: It is important to integrate core stability work and lower limb strengthening to minimize excessive stress on the shoulder complex, while optimizing force production and performance.

Background: Patients with circulatory failure have high mortality rates and require prompt assessment of microcirculation. Despite the improvement in hemodynamic parameters, microcirculatory dysfunction persists. We measured subcutaneous regional tissue oxygen saturation (rSO₂) with near-infrared spectroscopy (NIRS), which can assess microcirculation in patients with circulatory failure.

Methods: A finger-worn oximeter with NIRS measured rSO₂ in the forehead, thenar eminence, thumb, and knees. First, the rSO₂ was measured in healthy adult volunteers (n = 10). Circulatory failure was defined as a systolic blood pressure ≤ 90 mmHg and lactate ≥ 2 mmol/L. The study included 35 patients without circulatory failure and SOFA score of 0 at ICU admission and 38 patients with circulatory failure at ICU admission. Both groups included a single-center prospective study of patients who were transported to the ICU of the Nihon University Hospital. The rSO₂ was measured only on ICU admission in the non-circulatory failure group and later in the circulatory failure group.

Results: In the volunteer group, rSO₂ at each site was approximately 58%. The rSO₂ was significantly lower in the circulatory failure group than in the non-circulatory failure group (knee, p < 0.01). In the circulatory failure group, knee rSO₂ showed a significant negative correlation with SOFA score (Day 0, ρ = -0.37, p = 0.02; Day 1, ρ = -0.53, p < 0.01; Day 2, ρ = -0.60, p < 0.01).

Conclusions: Subcutaneous knee rSO₂ was associated with SOFA score and was considered an indicator of microcirculatory dysfunction and organ damage.