Mean Sensitivity Research Articles

Detecting and Quantifying Glaucomatous Visual Function Loss With Continuous Visual Stimulus Tracking: A Case-Control Study.

Continuous visual stimulus tracking could be used as an easy alternative to standard automated perimetry (SAP) for visual function screening. With continuous visual stimulus tracking, we simplified the perimetric task to following a moving dot on a screen with the eyes. Here, we determined whether tracking performance (the agreement between gaze and stimulus position) enables the detection and quantification of glaucomatous visual function loss (in terms of SAP), and whether it shows a learning effect. We evaluated the tracking performance of 36 cases with early, moderate, or severe glaucoma (median with interquartile range [IQR] age = 70 [67-74] years) and 36 controls (median = 70, IQR = 67-72 years). All participants monocularly tracked a moving stimulus (Goldmann size III) at 3 Weber contrast levels: 40, 160, and 640%, while their eye movements were recorded. Glaucoma decreased the tracking performance, with the most severe reduction in the severe glaucoma cases. A distinction between groups was possible, but depended on the contrast level: tracking performance of early glaucoma cases was significantly different from controls only at 40% contrast. Within the cases, glaucomatous visual function loss (SAP Mean Sensitivity [MS]) was best correlated with tracking performance when using 160% contrast. There was no significant learning effect. Overall, the data indicate that it is possible to detect and quantify glaucomatous visual function loss with continuous visual stimulus tracking. Continuous visual stimulus tracking is an easy, fast, and intuitive technique that has the potential for diagnostic applications in detection of new glaucoma cases and monitoring of previously diagnosed cases.

Visualization of retinal displacement after macular hole surgery and its correlation with postoperative visual function.

To observe retinal displacement following macular hole surgery using a three-dimensional barycenter localization algorithm and to evaluate its relationship with postoperative visual function. This prospective study included 28 eyes of 27 patients with idiopathic macular holes. A customized algorithm takes a weighted sum to locate the perimacular area (PMA) barycenter based on the magnitude of the distance between the barycenter and the intersection of retinal vessels. Retinal displacement distance, angle, and the rate of change in the PMA were calculated by comparing the barycenters in preoperative and postoperative photographs. Correlations between retinal displacement and various clinical parameters were also analyzed. The primary outcomes included retinal displacement characteristics (distance, angle, and rate of change in the PMA). Secondary outcomes were postoperative visual function, including best-corrected visual acuity, mean retinal sensitivity, and fixation stability. A significant "rebound" phenomenon, characterized by PMA wrinkling and extension, was observed in 11 cases (39.3%). The PMA was lower at 1 month postoperatively compared to that preoperatively (t=1.876, p=0.025). The mean displacement distance at 1 and 3 months postoperatively was 7.725 ± 0.097 and 6.187 ± 0.827, respectively. Mean retinal sensitivity at 3 months postoperatively improved compared to preoperative values (t=-2.171, p=0.039) and was significantly correlated with the rate of change in PMA at 3 months postoperatively (t=-2.162, p < 0.05). The three-dimensional barycenter localization algorithm effectively visualized retinal displacement. The PMA was shown to be associated with postoperative visual function in this analysis. The change trend of PMA and mean retinal sensitivity is consistent.

MRI imaging and machine learning based radiomics for detection of mixed HCC and CCA tumors: Still a need for liver biopsy?

531 Background: Primary liver cancer (PLC), comprising hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), is a leading cause of cancer mortality globally. The combined hepatocellular cholangiocarcinoma (cHCC-CC) subtype may be less common but is relevant to treatment efficacy. We therefore evaluated the diagnostic accuracy of various approaches in distinguishing these liver cancers. Methods: Patients diagnosed with HCC, CCA, and cHCC-CC at Beijing University Cancer Hospital and Institute, China were included. Radiologists of varying expertise independently assessed MRI scans, and we measured their diagnostic consistency. Radiomic features were extracted from MRI scans, and machine learning was applied to differentiate the cancer types. Results: Standard imaging was insufficient to reliably characterize cHCC-CC. Abdominal imaging experts (AIEs) had a higher mean sensitivity for HCC and CCA, 88% and 84% respectively, while non-experts (NIEs) had a lower sensitivity of 50% for HCC and 38% for CCA (HCC: p=0.03, CCA: p=0.008). Radiomic analysis found ‘Sphericity’ and ‘ClusterShade’ as the most relevant features. However, radiomics algorithms were also not sufficient to distinguish cHCC-CC from either HCC or CCA. Regarding sensitivity, the radiomic-based model was not better than radiologists for any of the three classes (p=0.065 for HCC, p=0.426 for CCA, and p=1.0 for cHCC-CC). The random forest algorithm yielded an accuracy of 76% in the test set, since it correctly classified most HCC and CCA, while only one quarter of cHCC-CC tumors. Conclusions: Until improved diagnostic tools are available, biopsy of liver cancer remains critical to the detection, diagnosis, and effective treatment of these cancers.

Assessment of photoreceptor recovery and visual function utilizing adaptive optics and microperimetry in patients with surgically closed macular holes.

This study investigated the association between photoreceptor structural restoration and visual function outcomes in patients undergoing surgery for closed macular holes (MHs). Using adaptive optics scanning laser ophthalmoscopy (AOSLO) and microperimetry, we aimed to provide a more detailed understanding of photoreceptor recovery and visual improvement in closed MHs. We conducted a retrospective observational study of 31 eyes of 28 patients who underwent vitrectomy with internal limiting membrane (ILM) peeling to treat idiopathic MHs. Visual and structural outcomes were monitored 3 months to 2 years postoperatively: best-corrected visual acuity (BCVA), MH diameter and duration, optical coherence tomography (OCT) images, 4° retinal mean sensitivity (RMS), fixation rates, 68.2 % BCEA (binary contour ellipse area) of microperimetry, and cone density as measured by AOSLO. Based on the OCT findings, the patients were categorized into two groups: type 1 (closed MH with no neurosensory defect at the fovea) and type 2 (closed MH with a foveal neurosensory defect). The analysis revealed that type 1 closures resulted in significantly better photoreceptor recovery, with cone cells migrating toward the fovea and forming dense islets, consistent with the integrity of the inner segment/outer segment layer on OCT. These patients also had better postoperative BCVA and higher RMS rather than fixation improvement, aligning with the improved cone density. In contrast, type 2 closures exhibited sparse cone distribution and a prevalence of large, high-density cells, possibly indicating glial cell proliferation and exposed retinal pigment epithelium (RPE). MH size, rather than duration or preoperative BCVA, was a key determinant of type 1 closure outcomes. AOSLO is a valuable tool for evaluating microstructural healing in MHs and reveals that type 1 closure, characterized by cone migration, leads to greater neuroretinal repair and improved BCVA and retinal sensitivity. Meanwhile, type 2 closures, marked by less photoreceptor recovery and higher glial proliferation, correspond to poorer visual function outcomes. This study highlights the role of photoreceptor density and migration in achieving optimal visual function post-surgery for MH closure.

A comparative study of statistical, radiomics, and deep learning feature extraction techniques for medical image classification in optical and radiological modalities.

Feature extraction in ML plays a crucial role in transforming raw data into a more meaningful and interpretable representation. In this study, we thoroughly examined a range of feature extraction techniques and assessed their impact on the binary classification models for medical images, utilizing a diverse and rich set of medical imaging modalities. Using H&E-stained, chest X-ray, and retina OCT images, we applied methods to extract statistical, radiomics, and deep features. These features were then used to develop PCA-LDA models as the employed classifier. We evaluated the models based on two decisive metrics: latency and performance. Latency measured the time taken for feature extraction and prediction, while mean sensitivity (balanced accuracy) characterizes the model performance. Our comparative study revealed that statistical and radiomics features were less effective for medical image classification, as they showed high latency and lower performance scores. In contrast, pre-trained DL networks performed efficiently, with high sensitivity and low latency. For H&E-stained images, the statistical feature extraction took about an hour and achieved 90.8% sensitivity, while ResNet50 reduced processing time fourfold and increased sensitivity to 96.9%. For chest X-rays, radiomics features were time-intensive with 92.2% sensitivity, while ResNet50 improved sensitivity to 96% with faster extraction time. For retina OCT images, radiomics yielded a sensitivity of 91%, while DenseNet121 achieved 98.6% sensitivity in 15min. These findings underscore the superior performance of DL techniques over the statistical and radiomics features, highlighting their potential for real-world applications where accurate and rapid diagnostic decisions are essential.

Detection of chronic wasting disease prions in the farm soil of the Republic of Korea.

Chronic wasting disease (CWD) is a highly contagious prion disease occurring in free-ranging and farmed cervids. CWD continues to spread uncontrolled across North America, and cases continue to be detected almost every year in the Republic of Korea. CWD-infected animals contaminate the soil by releasing infectious prions through their excreta, and shed prions accumulate and remain infectious in the soil for years. Given that the upper soil levels can become contaminated with prions and serve as infectivity reservoirs facilitating horizontal transmission of CWD, the ability to detect prions in the soil is needed for monitoring and managing CWD spread. Using the protein misfolding cyclic amplification (PMCA) technique, we investigated whether prions could be amplified and detected in farm soil experimentally exposed to CWD-infected brain homogenate as well as in the soil of CWD-affected farms. From each soil sample, we performed 10 serial extractions and used these 10 extracts as PMCA templates. Here, we show that prion seeding activity was detected in extracts from farm soil following 4 years of incubation with CWD-infected brain homogenate. More importantly, 13 of 38 soil samples collected from six CWD-affected farms displayed prion seeding activity, with at least one soil sample in each farm being PMCA positive. Mouse bioassays confirmed the presence of prion infectivity in the soil extracts in which PMCA seeding activity was detected. This is the first report describing the successful detection of prions in soil collected from CWD-affected farms, suggesting that PMCA conducted on serial soil extracts is a sensitive means for prion detection in CWD-contaminated soil.IMPORTANCEChronic wasting disease (CWD) is a highly contagious prion disease affecting free-ranging and farmed cervids. CWD continues to spread uncontrollably across North America, and multiple cases are detected annually in the Republic of Korea. Prions shed from CWD-infected animals remain infectious in the soil for years, serving as infectivity reservoirs that facilitate horizontal transmission of the disease. Therefore, the ability to detect CWD prions in soil is crucial for monitoring and managing the spread of the disease. In this study, we have demonstrated for the first time that prions in the soil of CWD-affected farms can be reliably detected using a combination of serial soil extraction and a prion amplification technique. Our data, in which at least one soil sample tested positive for CWD in each of the six CWD-affected farms analyzed, suggest that the approach employed in this study is a sensitive method for prion detection in CWD-contaminated soil.

Multicenter validation of secondary hemophagocytic lymphohistiocytosis diagnostic criteria.

Five fulfilled hemophagocytic lymphohistiocytosis (HLH)-2004 criteria, and the HScore are widely used and recommended by international expert consensus to diagnose secondary HLH. Both diagnostic scores have never been validated in heterogeneous patient cohorts of secondary HLH patients. We aimed to systematically optimize and validate diagnostic criteria of secondary HLH using a multicenter approach. We developed optimized criteria in our cohort of critically ill patients as a first step. We next validated these new criteria together with the original and modified HLH-2004 criteria as well as the HScore using original data of 13 published cohorts, which were identified by a systematic literature search. The best performing HLH diagnostic criteria sets over all 13 validation cohorts were the original HLH-2004 criteria with a decreased cut-off (cut-off 4, mean sensitivity 86.5%, mean specificity 86.1%), followed by the revised HLH-2004 criteria (natural killer cell activity removed; cut-off 4, mean sensitivity 83.8%, mean specificity 87.8%) and the HScore (cut-off 169, mean sensitivity 82.4%, mean specificity 87.6%). Our newly developed HLH diagnostic criteria showed inferior performance. Ferritin ≥500µg/L had 94.0% mean sensitivity over all cohorts. In this first multicenter validation study, four fulfilled HLH-2004 criteria and an HScore of 169 were suitable to diagnose secondary HLH, which will lead to rapid diagnosis and improved patient outcomes. Ferritin proved as a reliable HLH screening marker. Our results should be taken into account in clinical recommendations and in designing new studies.

Horizontal Gaze Tolerance and Its Effects on Visual Sensitivity in Glaucoma.

This study evaluates the effect of 6° horizontal gaze tolerance on visual field mean sensitivity (MS) in patients with glaucoma using a binocular head-mounted automated perimeter, following findings of structural changes in the posterior globe from magnetic resonance imaging and optical coherence tomography. In this cross-sectional study, a total of 161 eyes (85 primary open-angle glaucoma [POAG] and 76 healthy) from 117 participants were included. Logistic regression and 1:1 matched analysis assessed the propensity score for glaucoma and healthy eyes, considering age, sex, and axial length as confounders. Visual field tests were performed with the imo perimeter (CREWT Medical Systems, Inc., Tokyo, Japan) at central gaze, 6° abduction, and 6° adduction positions as fixation points. A mixed-effects model was used to compare MS under all conditions. The analysis included a total of 82 eyes, with 41 POAG and 41 healthy after matching. The mean (standard deviation) age was 68.0 (11.0) years, with a mean deviation of -9.9 (6.6)dB for POAG and -1.0 (1.9)dB for healthy eyes using Humphrey field analysis 24-2. MS did not significantly differ among central gaze (27.0 [1.8]dB), abduction (27.1 [1.9]dB), and adduction (26.9 [2.2]dB) in healthy eyes (P = 0.650). However, MS was significantly lower for adduction (17.2 [5.9]dB) compared to central gaze (18.1 [5.9]dB) and abduction (17.9 [5.9]dB) in glaucoma eyes (P = 0.001 and P = 0.022, respectively). Horizontal gaze, especially in adduction, significantly reduces visual sensitivity in glaucoma, suggesting a specific vulnerability associated with eye movement. This finding highlights the importance of eye positioning in glaucoma, warranting further investigation of its clinical significance.

External Validation of an AI mHealth Tool for Gingivitis Detection among Older Adults at Daycare Centers: A Pilot Study.

Periodontal disease is a significant public health concern among older adults due to its relationship with tooth loss and systemic health disease. However, there are numerous barriers that prevent older adults from receiving routine dental care, highlighting the need for innovative screening tools at the community level. This pilot study aimed first, to evaluate the accuracy of GumAI, a new mHealth tool that uses AI and smartphones to detect gingivitis, and the user acceptance of personalized oral hygiene instructions provided through the new tool, among older adults in day-care community centers. Participants were invited from 3 day-care community centers. Intraoral photographs were captured and assessed by both GumAI (test) and a panel consisting of 2 calibrated periodontists and a dentist (benchmark). Mean sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and F1 score were calculated to determine GumAI's diagnostic performance in comparison to the benchmark. User acceptance with this tool was assessed using 2 Rasch Theory-based 5-point Likert-type questions. 44 participants were recruited out of 80 invited older adults. GumAI demonstrated a sensitivity of 0.93 and specificity of 0.50 compared to the panel's assessments, with a PPV of 0.90 and NPV of 0.56. The accuracy and F1 scores were 0.85 and 0.91, respectively. All participants expressed high acceptance of the process. GumAI demonstrates high sensitivity, PPV, accuracy, and F1 score compared to the panel's assessments but falls relatively short in specificity and NPV. Despite this, the tool was highly accepted by older adults, indicating its potential to enhance gingivitis detection and oral hygiene management in community settings. Further refinements are necessary to improve specificity and validate usability measures. This study may pave the way for broader applications of mHealth systems in community settings, enabling greater health coverage and addressing oral health disparities.

Computed Tomography-Image-Based Glioma Grading Using Radiomics and Machine Learning: A Proof-of-Principle Study.

In recent years, numerous studies have been published on determining the WHO grade of central nervous system (CNS) tumors using machine learning algorithms. These studies are usually based on magnetic resonance imaging (MRI) and sometimes also on positron emission tomography (PET) images. To date, however, there are virtually no corresponding studies based on routinely generated computed tomography (CT) images. The aim of our proof-of-concept study is to investigate whether machine learning-based tumor diagnosis is also possible using CT images. We investigate the differentiability of histologically confirmed low-grade and high-grade gliomas. Three conventional machine learning algorithms and a neural net are tested. In addition, we analyze which of the common imaging methods (MRI or CT) appears to be best suited for the diagnostic question under investigation when machine learning algorithms are used. For this purpose, we compare our results based on CT images with numerous studies based on MRI scans. Our best-performing model includes six features and is obtained using univariate analysis for feature preselection and a Naive Bayes approach for model construction. Using independent test data, this model yields a mean AUC of 0.903, a mean accuracy of 0.839, a mean sensitivity of 0.807 and a mean specificity of 0.864. Our results demonstrate that low-grade and high-grade gliomas can be differentiated with high accuracy using machine learning algorithms, not only based on the usual MRI scans, but also based on CT images. In the future, such CT-image-based models can help to further accelerate brain tumor diagnostics and to reduce the number of necessary biopsies.

Development and assessment of a mortality risk prediction nomogram model for pneumocystis disease in ICU within 28 days

To develop and assess a nomogram predictive model for evaluating the 28-day mortality risk in patients diagnosed with Pneumocystis who have been admitted to the intensive care unit (ICU). From 2008 to 2022, clinical data on patients with Pneumocystis were collected using the American Critical Care Medical Information Database IV (MIMIC-IV). Initially, 63 significant predictive indicators were included, with ICU admission as the time node and all-cause mortality within 28 days as the outcome. Using complete data modeling, the variable selection approach combines two methods: Lasso regression (glmnet package) and collinearity screening (car package). Use the bootstrap method 1000 times for internal validation. Calculate the AUC, mean sensitivity, and specificity of 1000 resamplings, as well as the 95% confidence interval, and then plot the ROC, calibration, and DCA curves. The patients were split into two groups based on their 28-day survival status: 83 cases (67.48%) in the survival group and 40 instances (32.52%) in the death group. Five variables—the history of malignant tumors, Lods scores, Oasis scores, and complications of shock and severe renal injury—were eventually included in the entire sample after screening. According to Receiver Operating Characteristic (ROC) analysis, the model’s sensitivity was 0.600 (95% CI 0.448–0.752), specificity was 0.904 (95% CI 0.840–0.967), and AUC was 0.814 (95% CI 0.732–0.897). The DCA curve indicates that the model application has high accuracy, which leads to a net benefit for population prediction, and the model calibration curve demonstrates good calibration accuracy. The bootstrap model’s 1000 internal validation results demonstrate that the model’s calibration performance is good and that its accuracy is highest when the probability of patient outcome events falls between 35 and 60%, which yields the highest net benefit for population prediction. This study developed a nomogram utilizing MIMIC-IV clinical big data to predict the 28-day mortality risk in patients with Pneumocystis disease. Incorporating five critical factors, the nomogram offers a user-friendly, visual method for calculating personalized risk scores based on patient-specific information, including medical history, laboratory results, and clinical scores. Demonstrating robust discrimination and calibration, this tool provides clinicians with a valuable resource for assessing prognosis and making evidence-based treatment decisions.

Urgent findings in CT chest examinations: Radiography and medical students' capabilities and learning perspectives - a preliminary study.

Radiography and medical students (RMS), upon graduation, require capabilities to provide life-saving care through identification and communication of urgent findings on radiological imaging. This preliminary study investigated RMS' ability to identify and categorise urgent findings on CT examinations. It also explored their experiences of image interpretation education. A sequential explanatory mixed-methods study was employed. Participants were students who had recently completed Year-4 (radiography/medicine) or Year-2 (medicine-only) from three Australian universities. Urgent finding identification capabilities were assessed via a test-set of 10 CT examinations with a range of findings (normal, abnormal but non-urgent, abnormal but urgent) that was developed on a validated learning and assessment platform. Each case required selection of: normal versus abnormal; where abnormal, finding/s from a pre-defined list; urgency level; self-rated confidence. Learning experiences were then explored via a survey (Likert statements, free-text responses). Mean sensitivity, specificity and accuracy (with min‒max ranges), respectively, were: All participants [n=30] 0.87 (0.60-1.0), 0.63 (0.40-1.0), 0.75 (0.50-1.0); 4th-year radiography [n=17] 0.89 (0.6-1.0), 0.66 (0.4-1.0), 0.78 (0.60-1.0); 4th-year medicine [n=8] 0.85 (0.6-1.0), 0.50 (0.4-0.6), 0.68 (0.50-0.8); 2nd-year medicine [n=5] 0.80 (0.60-1.0), 0.75 (0.75-1.0), 0.78 (0.78-0.78). False positives were highest for cases with non-urgent abnormalities in all groups. Free-text responses revealed students' desire for dedicated urgent finding educational resources with high 'repetitive learning' effects. RMS demonstrated considerable performance in identifying urgent abnormalities as a group, but individual capabilities varied from pass-level to perfect. Together, participants demonstrated limited ability to correctly classify non-urgent CT chest abnormalities. This study highlights opportunities for targeted urgent-finding resource development, focused on improving consistency within cohorts and reducing false positive rates.

Proximity Ligation Assay to Study Oncogene-Derived Transcription-Replication Conflicts.

Both DNA replication and RNA transcription utilize genomic DNA as their template, necessitating spatial and temporal separation of these processes. Conflicts between the replication and transcription machinery, termed transcription-replication conflicts (TRCs), pose a considerable risk to genome stability, a critical factor in cancer development. While several factors regulating these collisions have been identified, pinpointing primary causes remains difficult due to limited tools for direct visualization and clear interpretation. In this study, we directly visualize TRCs using a proximity ligation assay (PLA), leveraging antibodies specific to PCNA and phosphorylated CTD of RNA polymerase II. This approach allows precise measurement of TRCs between replication and transcription processes mediated by RNA polymerase II. The method is further enhanced through DNA primers conjugated covalently to these antibodies, coupled with PCR amplification using fluorescent probes, providing a highly sensitive and specific means of detecting endogenous TRCs. Fluorescence microscopy enables the visualization of these conflicts, offering a powerful tool to study genome instability mechanisms associated with cancer. This technique addresses the gap in direct TRC visualization, allowing for a more comprehensive analysis and understanding of the underlying processes driving genome instability in cells.

Machine learning models provide modest accuracy in predicting clinical impact of porcine reproductive and respiratory syndrome type 2 in Canadian sow herds.

To determine the predictive potential of the open reading frame 5 nucleotide sequence of porcine reproductive and respiratory syndrome (PRRS) virus and the basic demographic data on the severity of the impact on selected production parameters during clinical PRRS outbreaks in Ontario sow herds. A retrospective longitudinal study of clinical outbreaks in Ontario sow herds at various points between September 5, 2009, and February 5, 2019, was conducted using herds as units of analysis. Data were gathered from study sow farms in Ontario at the start of each clinical outbreak. Six machine learning models and 2 different genetic input structures of open reading frame 5 sequences were utilized to predict the impact on abortion and preweaning mortality. Extreme boosting machine learning models with genetic data represented through 2-dimensional multiple correspondence analysis had the highest accuracy when predicting clinical outcomes (60.8% [SD = 12.4%] and 74.4% [SD = 13.2%]) for abortion and preweaning mortality outcomes, respectively. The mean sensitivity of classifying outbreaks with a high impact on abortion was 50%, with a specificity of 89.2%. The mean sensitivity of classifying outbreaks with high preweaning mortality was 56.2%, with a specificity of 85.2%. The data and methods utilized herein exhibited improvement in accuracy over the baseline; however, this increase was not sufficient to warrant field implementation. Predictive models based on observed data could assist practitioners in linking the genetics of the PRRS virus with clinical impact in clinical settings. Models trained in this study show promise for PRRS clinical impact prediction.

Contrast sensitivity before and after refractive error correction among refractive error patients in Hawassa, Sidama, Ethiopia 2023

BackgroundContrast sensitivity is an important measure of vision quality. Risk of falling injury is strongly associated with poor contrast sensitivity compared with poor near- and distance visual acuity. Since good visual acuity is not necessarily associated with good visual performance in the “real world” it is important to consider contrast sensitivity when prescribing corrective lenses. There is limited evidence on how refractive error and its correction affect contrast sensitivity in the study area. Therefore, this study is important to understand how refractive correction affects contrast sensitivity, which is important baseline information for prescribing refractive error correction.Objective of this studyThis study aimed to measure the mean difference in contrast sensitivity with and without corrective lenses in patients with refractive error.MethodsObjective and subjective refractions were done for each patient, then Pelli- Robson contrast sensitivity was measured for them using computer based Pelli-Robson chart at 1 m. Pelli- Robson contrast sensitivity value (Log CS) with and without corrective lenses were recorded for each study participant. The collected data were checked for completeness and analyzed using SPSS 26. Descriptive analyses, independent sample t-tests, and one-way ANOVA were performed to compare CS means; the findings are presented using tables and pie charts. A P-value of ≤ 0.05 was used to indicate statistical significance.ResultsOne hundred sixty-two patients attending refraction clinics participated in this study with response rate 98.19% of which 51.9% were male. The mean age of the participants was 34.28 ± 12.79 (SD). The mean contrast sensitivity increased from 1.29 ± 0.35 (SD) to1.51 ± 0.28 (SD) LogCS units with refractive correction (p = 0.000). There is no significant difference in contrast sensitivity between the types of refractive errors.Conclusion and recommendationUncorrected refractive can affect contrast sensitivity; therefore, proper refractive correction is essential to improve visual acuity and contrast sensitivity.

Retinal Sensitivity in KCNV2-Associated Retinopathy.

The purpose of this study was to analyze the retinal sensitivity under photopic, mesopic, and scotopic conditions in a cohort of patients affected with KCNV2-associated retinopathy. Cross-sectional evaluation of molecularly confirmed individuals was conducted. Data were obtained prospectively. The main microperimetry parameters analyzed were mean sensitivity (MS) and number of floor effects (noF) in the central macular zone (CMZ; central 6degrees) and peripheral macular zone (PMZ; 6 to 10degrees). Interocular symmetry was also assessed. The MS under photopic, mesopic, and scotopic conditions were, respectively, 20.2 (6.3-29.5, ±6.5), 16.2 (3.7-23.1, ±5.3), and 1.3 (0-13.3, ±3.8) in the right eyes, and 21.1 (9.8-28.9, ±6.1), 16.8 (9.6-23.5, ±4.0), and 1.1 (0-12.4, ±3.5) in the left eyes. MS was highly symmetric between eyes (Pearson correlation coefficient) in photopic (r = 0.96, P < 0.0001), mesopic (r = 0.9, P < 0.0001), and scotopic (r = 0.98, P < 0.0001) testing conditions. MS was also highly symmetric (Pearson coefficient) in the photopic CMZ (r = 0.97, P = 0.0001) and PMZ (r = 0.95, P = 0.0002), mesopic CMZ (r = 0.94, P = 0.0003), and PMZ (r = 0.92, P = 0.0009), and scotopic CMZ (r = 0.99, P < 0.0001) and PMZ (r = 0.99, P < 0.0001).The mean noF (± standard deviation [SD]; range) was 4.6 (SD = ±5.3, range = 0-16) in photopic, 5.0 (SD = ±5.4, range = 0-15) in mesopic, and 23.2 (SD = ±8.6, range = 0-28) in scotopic conditions. Subject 01-041 was the only affected individual in which no floor effect was found. Simple linear regression revealed a significant inverse relationship between age and MS, and a direct relationship between age and noF. KCNV2-associated retinopathy is a largely symmetric disease from a functional perspective. Despite the early decrease in scotopic retinal sensitivity, our data suggests a large window for photoreceptor rescue with novel treatments, such as gene therapy.

A deep learning approach versus expert clinician panel in the classification of posterior circulation infarction.

Posterior circulation infarction (POCI) is common. Imaging techniques such as non-contrast-CT (NCCT) and diffusion-weighted-magnetic-resonance-imaging commonly fail to detect hyperacute POCI. Studies suggest expert inspection of Computed Tomography Perfusion (CTP) improves diagnosis of POCI. In many settings, there is limited access to specialist expertise. Deep-learning has been successfully applied to automate imaging interpretation. This study aimed to develop and validate a deep-learning approach for the classification of POCI using CTP. Data were analysed from 3541-patients from the International-stroke-perfusion-registry (INSPIRE). All patients with baseline multimodal-CT and follow-up imaging performed at 24-48 h were identified. A cohort of 541-patients was constructed on a 1:3 POCI-to -reference-ratio for model analysis. A 3D-Dense-Convolutional-Network (DenseNet) was trained to classify patients into POCI or non-POCI using CTP-deconvolved-maps. Six-stroke-experts also independently classified patients based upon stepwise access to multimodal CT (mCT) data. DenseNet results were compared against expert clinician results. Model and clinician performance was evaluated using area-under-the-receiver-operating-curve, sensitivity, specificity, accuracy and precision. Clinician agreement was measured with the Fleiss-Kappa-statistic. Best mean clinician diagnostic accuracy, sensitivity and agreement was demonstrated after review of all mCT data (AUC: 0.81, Sensitivity: 0.65, Fleiss-Kappa-statistic: 0.73). There was a spectrum of individual clinician results with an AUC-range of 0.73-0.86. Best DenseNet performance was recorded with an input combination of NCCT and delay-time maps. The DenseNet model was superior to the best mean clinician performance (AUC: 0.87) and was due to enhanced sensitivity (DenseNET: 0.77, Clinician: 0.65). The degree to which the DenseNet model outperformed each clinician ranged and was clinician specific (AUC improvement 0.01-0.14). Comprehensive review of CTP improves diagnostic performance and agreement amongst clinicians. A DenseNet model was superior to best mean clinician performance. The degree of improvement varied by specific clinician. Development of a clinician-DenseNet approach may improve inter-clinician agreement and diagnostic accuracy. This approach may alleviate limited specialist services in resource constrained settings.

Raman spectroscopic fingerprinting for the identification and quantitative analysis of sports doping β-agonists based on gold nanopolyhedra.

Beta-stimulant, that is, β-adrenergic stimulant, also known as β-agonists, is bioactive catecholamine compounds naturally produced in animals' adrenal medulla glands that induce relaxation in asthmatic airway smooth muscles upon inhalation while also temporarily boosting athletic alertness and alleviating fatigue. However, their potential for dependency poses health risks including unnoticed exacerbation leading to severe illness or fatality prompting their inclusion on WADA's prohibited substances list. Surface-enhanced Raman spectroscopy (SERS) offers a rapid, sensitive, and label-free means for identifying characteristic peaks associated with β-agonist compounds. In our investigation, the utilization of seed-mediated synthesis technique facilitated production of gold polyhedral substrates serving as highly sensitive biosensors for detecting β-agonist presence. The unique geometry of these particles generates enhanced SERS signals due to their multiple corners, resulting low detection limits (9.33×10-7, 6.28×10-7, and 6.19×10-7 g·mL-1 for salbutamol, clenbuterol and higenamine respectively), with promising prospects for improving overall sensitivity in β-agonists detection.

Photon-Counting CT Effects on Sensitivity for Liver Lesion Detection: A Reader Study Using Virtual Imaging.

Background Detection of hepatic metastases at CT is a daily task in radiology departments that influences medical and surgical treatment strategies for oncology patients. Purpose To compare simulated photon-counting CT (PCCT) with energy-integrating detector (EID) CT for the detection of small liver lesions. Materials and Methods In this reader study (July to December 2023), a virtual imaging framework was used with 50 anthropomorphic phantoms and 183 generated liver lesions (one to six lesions per phantom, 0.4-1.5 cm in diameter). Virtual CT platforms simulated PCCT and EID CT scanners. Phantoms were virtually scanned using routine (6 mGy) and low-dose (1.5 mGy) conditions and reconstructed with three kernels. A subset of 300 scans (150 PCCT vs EID CT pairs) were selected. Four radiologists independently reviewed all scans to mark liver lesions, assigned confidence scores for detection, and rated scan quality. Analysis was performed on a per-lesion basis to determine sensitivity for several variables and on a per-scan basis for scan quality. The McNemar test, two-sided paired t tests, and mixed-effects logistic regression models were fitted; P < .05 was considered indicative of statistically significant difference. Results Consensus reader sensitivity in detecting lesions was 82.1% (451 of 549) for PCCT versus 77.6% (426 of 549) for EID CT (P < .001), with a mean sensitivity gain of 4.3 percentage points ± 1.3 (P < .001 to P = .02 per reader). Readers had better subjective confidence for lesions at PCCT (mean score, 61.5 ± 22 vs 56.1 ± 24 [on a 101-point scale]; P < .001). Sensitivity was lower for lesions smaller than 1 cm, with more pronounced difference between PCCT and EID CT (74.0% [271 of 366] vs 67.2% [246 of 366]; P < .001). At the lower dose level, PCCT showed higher sensitivity than EID CT (68.9% [168 of 244] vs 61.1% [149 of 244]; P < .001) for subcentimeter lesions. In a multivariable model, PCCT was independently associated with increased odds of lesion detection (odds ratio, 1.55; P < .001). Image quality was slightly higher for PCCT (mean score, 55.3 vs 50.6 [on a 101-point scale]; P < .001). Conclusion Compared with EID CT, PCCT showed better sensitivity in the detection of small liver lesions. © RSNA, 2025 Supplemental material is available for this article. See also the editorial by Menu in this issue.

Use of value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment

The paper demonstrates the potential for using value-based and motivational parameters with artificial intelligence technology to predict cadet maladjustment. A retrospective cohort study was conducted. For 2013–2021, 734 cadets of the Navy Military Training and Research Center “Soviet Union Fleet Admiral N.G. Kuznetsov Naval Academy” were examined, 48 of them were diagnosed with maladjustment. Neural networks were used for mathematical modeling of maladjustment prediction. The study included 8 cycles of neural network training and 7 cycles of neural network model testing. As the actual material increases, the sensitivity of the model for predicting cadet maladjustment using neural networks increases: 30.MLP 16-7-2; 28.MLP 16-13-2; 30.MLP 16-22-2; 29.MLP 16-31-2; 42.MLP 16-39-2; 19.MLP 16-45-2; 16.MLP 16-48-2; 30.MLP 16-30-2 from 0.43 to 1.00 conventional units (y = 0.017x2 – 0.0647x + 0.4898, R² = 0.8264); specificity: from 0.96 to 1.00 conventional units (y = –0.002x2 + 0.0211x + 0.9462, R² = 0.8923); predictive value increased from 91.8% to 99.45% (y = –0.1477x2 + 2.3309x + + 90.238, R² = 0.9368). When the models were tested on new samples, the mean sensitivity was 0.45 conventional units with an increasing trend (y = 0.0207x2 – 0.1214x + 0.5271, R² = 0,6945), specificity: 0.97 conventional units (y = –0.0048x2 + + 0.0388x + 0.9086, R² = 0.772), predictive value: 92.6% (y = –0.4962x2 + 3.5402x + 88.447, R² = 0.6598). Therefore, the model for predicting cadet maladjustment using neural networks can identify cadets who will experience maladjustment with an accuracy of 32% to 72%, whereas no more than 6% of cadets without maladjustment will receive a false prediction. The predictive value of the model is close to the absolute accuracy of vocational aptitude prediction with reference values of 65%–70%. The predictive ability of the models tested in the study, ranging from 89.7% to 96.4%, confirms the high effectiveness of using neural networks to predict maladjustment. The value-based and motivational parameters of the cadets, combined with the use of neural networks to predict their maladjustment, create a highly effective artificial intelligence system. Such an approach can be used in medical and psychological support activities for military personnel at a military university for their optimal selection and support.