Diagnostic Performance Research Articles

Diagnostic performance of rapid on‐site evaluation during bronchoscopy for lung cancer: A comprehensive meta‐analysis

AbstractBackgroundLung cancer is the leading cause of cancer‐related mortality worldwide. Screening high‐risk populations for lung cancer with low‐dose computed tomography (LDCT) reduces lung cancer mortality. Bronchoscopy is a diagnostic procedure used to monitor patients suspected of having lung cancer after LDCT. Rapid on‐site evaluation (ROSE) can improve the diagnostic accuracy of endobronchial ultrasound–guided transbronchial needle aspiration (EBUS‐TBNA), although its diagnostic value remains unclear. In this meta‐analysis, the authors evaluated the diagnostic accuracy of ROSE during bronchoscopy.MethodsThe PubMed, Embase, and Cochrane Library databases were searched for studies evaluating the diagnostic accuracy of ROSE for lung cancer during bronchoscopy. Studies evaluating the performance of ROSE and articles providing sufficient data for constructing a 2 × 2 table on a per‐lesion basis were included. A meta‐analysis was conducted using a bivariate random‐effects model.ResultsIn total, 32 studies involving 8243 lung lesions were included with a pooled sensitivity of 91.8% and a pooled specificity of 94.9%. Subgroup analysis of 12 studies involving 2929 specimens from patients who underwent computed tomography revealed a pooled sensitivity of 93.8% and a pooled specificity of 96%. Further subgroup analysis of seven studies on the diagnostic outcomes of ROSE for intrathoracic or mediastinal lymph nodes through EBUS‐TBNA for lung cancer staging revealed a pooled sensitivity of 90.1% and a pooled specificity of 96.9%.ConclusionsROSE exhibited high sensitivity and specificity for diagnosing lung cancer during bronchoscopy. It also exhibited high sensitivity in detecting lung cancer in patients undergoing LDCT and higher specificity for nodal staging with EBUS‐TBNA.

A CT-based radiomics predictive nomogram to identify pulmonary tuberculosis from community-acquired pneumonia: a multicenter cohort study

PurposeTo develop a predictive nomogram based on computed tomography (CT) radiomics to distinguish pulmonary tuberculosis (PTB) from community-acquired pneumonia (CAP).MethodsA total of 195 PTB patients and 163 CAP patients were enrolled from three hospitals. It is divided into a training cohort, a testing cohort and validation cohort. Clinical models were established by using significantly correlated clinical features. Radiomics features were screened by the least absolute shrinkage and selection operator (LASSO) algorithm. Radiomics scores (Radscore) were calculated from the formula of radiomics features. Clinical radiomics conjoint nomogram was established according to Radscore and clinical features, and the diagnostic performance of the model was evaluated by receiver operating characteristic (ROC) curve analysis.ResultsTwo clinical features and 12 radiomic features were selected as optimal predictors for the establishment of clinical radiomics conjoint nomogram. The results showed that the predictive nomogram had an outstanding ability to discriminate between the two diseases, and the AUC of the training cohort was 0.947 (95% CI, 0.916-0.979), testing cohort was 0.888 (95% CI, 0.814-0.961) and that of the validation cohort was 0.850 (95% CI, 0.778-0.922). Decision curve analysis (DCA) indicated that the nomogram has outstanding clinical value.ConclusionsThis study developed a clinical radiomics model that uses radiomics features to identify PTB from CAP. This model provides valuable guidance to clinicians in identifying PTB.

Diagnostic accuracy of hemodynamic assessment of intracranial atherosclerotic stenosis from a single angiographic view: a validation study

BackgroundThe aim of this study is to assess the feasibility of identifying the hemodynamic status of intracranial atherosclerotic stenosis (ICAS) using angio-based fractional flow (FF) calculated from a single angiographic...

Clinical utility of electrocardiographic voltage parameters for the diagnosis of hypertrophic cardiomyopathy

BackgroundWhile electrocardiographic parameters of hypertensive left ventricular hypertrophy (H-LVH) are well known, limited data are available regarding hypertrophic cardiomyopathy (HCM). This study was to assess the diagnostic value of electrocardiographic voltage parameters in HCM.MethodsIncluded patients with HCM treated between March 2015 and May 2023. Voltage parameters (S-L, Cornell, Cornell product, Lewis, Peguero, and modified Cornell voltages) and echocardiography were evaluated. Receiver operating characteristic (ROC) curves were used to assess the diagnostic value of electrocardiogram in HCM. The multiple linear regression was conducted to analyze the correlation between electrocardiogram indicators and cardiac diastolic function.ResultsThe highest sensitivity for HCM was Peguero voltage (70.4%; 88.6% specificity). The Peguero voltage had high sensitivity in male (63.8%) and female patients (84.9%), those aged <65 years (71.6%) and ≥65 years (68.3%), with non-apical HCM (AHCM) (76.7%), obstructive HCM (82.1%), and non-obstructive HCM (66.9%). The sensitivity of the S-L voltage was high in AHCM (72.2%). The sensitivity for HCM reached 88.7% when the S-L and Peguero voltages were combined. The modified Cornell index had the highest area under the curve (0.88, 95% CI: 0.84–0.91), and its optimal cutoff value was 2.05 mV in males (77.6% sensitivity and 74% specificity) and 1.935 mV in females (90.6% sensitivity and 91.4% specificity). Peguero voltage (beta = 0.154, P = 0.034) and SD (beta = 0.223 P = 0.004) were independently correlated with E/e’, an index of left ventricular diastolic function.ConclusionThe Peguero voltage had high sensitivity and specificity for detecting the presence of HCM. It was positively correlated with E/e’ in patients with HCM. For AHCM, the S-L voltage was more advantageous. Combining the S-L voltage with the Peguero voltage further improves the sensitivity for HCM and thus could be used to improve the screening of HCM in clinical practice. The SD and modified Cornell voltage also had good diagnostic performance, especially in females.

Latent tuberculosis infection and diagnostic performance of the tuberculin skin test among type 2 diabetics in Sana’a city, Yemen

BackgroundTuberculosis (TB) is one of the most widespread infectious diseases worldwide, typically persisting in the body as a latent TB infection (LTBI). Patients with type 2 diabetes have an increased risk of LTBI progressing to active TB. Therefore, this study determined the prevalence and predictors of LTBI and assessed the agreement between tuberculin skin test (TST) and interferon-gamma release assay (IGRA) in diagnosing LTBI among type 2 diabetics in Sana’a city, Yemen.MethodsA cross-sectional study was conducted among 150 type 2 diabetics in private health facilities in Sana’a in 2023. Data about demographics, diabetes-related characteristics, and potential risk factors for LTBI were collected using a structured questionnaire. Patients were then screened for LTBI using TST and IGRA. Univariate analysis was used to identify LTBI-associated risk factors, and multivariable binary logistic regression was used to identify independent predictors of LTBI. The agreement between TST and IGRA for diagnosing LTBI was assessed using Cohen’s kappa coefficient (κ).ResultsLTBI was prevalent among 29.3% of type 2 diabetics using both types of tests (25.3% with IGRA and 21.3% with TST). Male gender was an independent predictor of LTBI (AOR = 4.4, 95% confidence interval: 1.30–15.08; P = 0.018). However, being employed (AOR = 0.3, 95% CI: 0.09–0.75; P = 0.013) and longer duration since diabetes diagnosis (AOR = 0.3, 95% CI: 0.12–0.98; P = 0.046) were identified as predictors of lower LTBI risk. The agreement between TST and IGRA for the diagnosis of LTBI was 88%, with a good and statistically significant agreement between the two test types (κ = 0.670; P < 0.001).ConclusionsLTBI is common among type 2 diabetics seeking medical care in Sana’a city, with about one-third of them possibly being latently infected. A higher LTBI risk can be predicted among males, while a lower risk can be predicted among those employed or being diagnosed with diabetes for at least five years. The TST shows good agreement with IGRA in diagnosing LTBI among type 2 diabetics, supporting its continued use as a cost-effective and easily accessible test for diagnosing LTBI in the country.

A novel cystatin in Psoroptes ovis var. cuniculi: molecular characterization, serodiagnostic potential, and its anti-inflammatory property on rabbit peripheral blood mononuclear cells

BackgroundThe ectoparasite Psoroptes ovis var. cuniculi causes substantial economic losses to the global rabbit industry. Currently, microscopy for identifying Psoroptes mite in skin scrapings, as the “diagnosis gold standard,” remains a challenge owing to its poor sensitivity in detecting low-level and/or early stage mite infestations. Additionally, Psoroptes infestations rapidly trigger cutaneous inflammation, thus the mites might produce some molecules to deal with the harmful effects of inflammation for their long-time survival on the host skin, but these molecules are still mostly unknown.MethodsTo seek a sensitive diagnostic method and illuminate the new antiinflammatory molecules, we characterized a novel cystatin of P. ovis var. cuniculi (PsoCys) using bioinformatics and molecular biology methods.ResultsThe results showed that PsoCys comprised the classical features of the type II cystatin superfamily including an N-terminal glycine residue, a central QXVXG motif, and a C-terminal LW motif. In mixed stages of mites, the transcription level of PsoCys was significantly higher in “fed” mites than in “starved” mites (P < 0.001), and among the different life-cycle stages of “fed” mites, the expression of PsoCys was higher in adult males than in larva, nymph, and adult females (P < 0.001). The established indirect ELISA based on recombinant PsoCys (rPsoCys-iELISA) presented 95.4% sensitivity and 95.7% specificity. The area under the receiver operating characteristic curve (AUC) for this method was 0.991, indicating its excellent diagnostic performance. Moreover, rPsoCys-iELISA had advantages over microscopy for detecting low-level and/or early stage mite infestations (90% versus 40% in artificial infestation cases at 3 weeks post-infestation; 61.9% versus 22.6% in clinical cases). In addition, rPsoCys could inhibit the activity of papain and cathepsin B in vitro, and significantly suppressed mRNA levels of toll-like receptors (TLR 1, 2, 4, and 6) and downstream molecules (NF-κB, p38, MyD88, IL-10, and IFN-γ) in LPS-stimulated rabbit PBMCs, indicating its anti-inflammatory property.ConclusionsOur findings indicated that PsoCys was a novel type II cystatin of Psoroptes mites, and it served as a potential serological diagnostic antigen for detecting low-level and/or early stage mite infestations, as well as a novel anti-inflammatory molecule of Psoroptes mites.Graphical abstract

Diagnostic performance of an automated robot for MALDI target preparation in microbial identification

ABSTRACT The MBT Pathfinder is an automated colony-picking robot designed for efficient sample preparation in matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry. This article presents results from three key experiments evaluating the instrument’s performance in conjunction with MALDI Biotyper instrument. The method comparison experiment assessed its clinical performance, demonstrating comparable results with gram-positive, gram-negative, and anaerobic bacteria (scores larger than 2.00) and superior performance over simple direct yeast transfer (score: 1.80) when compared to samples prepared manually. The repeatability experiment confirmed consistent performance over multiple days and labs (average log score: 2.12, std. deviation: 0.59). The challenge panel experiment showcased its consistent and accurate performance across various samples and settings, yielding average scores between 1.76 and 2.19. These findings underline the MBT Pathfinder as a reliable and efficient tool for MALDI-TOF mass spectrometry sample preparation in clinical and research applications.

Screening and identification of the hub genes in severe acute pancreatitis and sepsis

BackgroundSevere acute pancreatitis (SAP) is accompanied with acute onset, rapid progression, and complicated condition. Sepsis is a common complication of SAP with a high mortality rate. This research aimed to identify the shared hub genes and key pathways of SAP and sepsis, and to explore their functions, molecular mechanism, and clinical value.MethodsWe obtained SAP and sepsis datasets from the Gene Expression Omnibus (GEO) database and employed differential expression analysis and weighted gene co-expression network analysis (WGCNA) to identify the shared differentially expressed genes (DEGs). Functional enrichment analysis and protein–protein interaction (PPI) was used on shared DEGs to reveal underlying mechanisms in SAP-associated sepsis. Machine learning methods including random forest (RF), least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) were adopted for screening hub genes. Then, receiver operating characteristic (ROC) curve and nomogram were applied to evaluate the diagnostic performance. Finally, immune cell infiltration analysis was conducted to go deeply into the immunological landscape of sepsis.ResultWe obtained a total of 123 DEGs through cross analysis between Differential expression analysis and WGCNA important module. The Gene Ontology (GO) analysis uncovered the shared genes exhibited a significant enrichment in regulation of inflammatory response. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the shared genes were primarily involved in immunoregulation by conducting NOD-like receptor (NLR) signaling pathway. Three machine learning results revealed that two overlapping genes (ARG1, HP) were identified as shared hub genes for SAP and sepsis. The immune infiltration results showed that immune cells played crucial part in the pathogenesis of sepsis and the two hub genes were substantially associated with immune cells, which may be a therapy target.ConclusionARG1 and HP may affect SAP and sepsis by regulating inflammation and immune responses, shedding light on potential future diagnostic and therapeutic approaches for SAP-associated sepsis.

When time is of the essence: ethical reconsideration of XAI in time-sensitive environments

The objective of explainable artificial intelligence systems designed for clinical decision support (XAI-CDSS) is to enhance physicians’ diagnostic performance, confidence and trust through the implementation of interpretable methods, thus providing...

Diagnostic performance of pupil perimetry in detecting hemianopia under standard and virtual reality viewing conditions.

To determine the diagnostic performance and reliability of two pupil perimetry (PP) methods in homonymous hemianopia. This cross-sectional monocenter cohort study performed gaze-contingent flicker PP (gcFPP) and a virtual reality version of gcFPP (VRgcFPP) twice on separate occasions in all patients suffering from homonymous hemianopia due to neurological impairment. The main outcomes were (1) test accuracy and (2) test-retest reliability: (1) was measured through area under the receiver operating characteristics curve (AUC) calculation of (VR)gcFPP results with comparators being SAP and healthy controls, respectively; (2) was evaluated by comparing tests 1 and 2 of both methods within patients. Both gcFPP and VRgcFPP were performed in 15 patients (12 males, MAge = 57, SDAge = 15) and 17 controls (6 males, MAge = 53, SDAge = 12). Mean test accuracy was good in separating damaged from intact visual field regions (gcFPP: Mauc = 0.83, SDauc = 0.09; VRgcFPP: Mauc = 0.69,SDauc = 0.13) and in separating patients from controls (gcFPP: Mauc = 0.92,SDauc = 0.13; VRgcFPP: Mauc = 0.96,SDauc = 0.15). A high test-retest reliability was found for the proportion intact versus damaged visual field (gcFPP: r = 0.95, P < .001, VRgcFPP: r = 1.00, P < .001). Overall, these results can be summarized as follows: (1) the comparison of pupil response amplitudes between intact versus damaged regions per patient indicate that gcFPP allows for cleaner imaging of intact versus damaged visual field regions than VRgcFPP, (2) the comparisons of average differences in intact versus damaged amplitudes between patients and controls demonstrate high diagnostic performance of both gcFPP and VRgcFPP, and (3) the test-retest reliabilities confirm that both gcFPP and VRgcFPP reliably and consistently measure defects in homonymous hemianopia. KEY MESSAGES: What is known Standard automated perimetry is the current gold standard for visual field examination, but not always suited for the evaluation of the VF in neurologically impaired patients. Pupil perimetry consists of the measurement of pupillary responses to light stimuli as a measure of visual sensitivity. What is new This study reports the highest diagnostic accuracy of pupil perimetry so far in patients with homonymous hemianopia. Gaze-contingent flicker pupil perimetry reliably and consistently measures defects in homonymous hemianopia under standard and virtual reality viewing conditions.

Identification of amino acids metabolomic profiling in human plasma distinguishes lupus nephritis from systemic lupus erythematosus

Lupus nephritis (LN) is an immunoinflammatory glomerulonephritis associated with renal involvement in systemic lupus erythematosus (SLE). Given the close relationship between plasma amino acids (AAs) and renal function, this study aimed to elucidate the plasma AA profiles in LN patients and identify key AAs and diagnostic patterns that distinguish LN patients from those with SLE and healthy controls. Participants were categorized into three groups: normal controls (NC), SLE, and LN. Ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed to quantify AA levels in human plasma. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were utilized to identify key AAs. The diagnostic capacity of the models was assessed using receiver operating characteristic (ROC) curve analysis and area under the ROC curve (AUC) values. Significant alterations in plasma AA profiles were observed in LN patients compared to the SLE and NC groups. The OPLS-DA model effectively separated LN patients from the SLE and NC groups. A joint model using histidine (His), lysine (Lys), and tryptophan (Trp) demonstrated exceptional diagnostic performance, achieving an AUC of 1.0 with 100% sensitivity, specificity, and accuracy in predicting LN. Another joint model comprising arginine (Arg), valine (Val), and Trp also exhibited robust predictive performance, with an AUC of 0.998, sensitivity of 93.80%, specificity of 100%, and accuracy of 95.78% in distinguishing between SLE and LN. The joint forecasting models showed excellent predictive capabilities in identifying LN and categorizing lupus disease status. This approach provides a novel perspective for the early identification, prevention, treatment, and management of LN based on variations in plasma AA levels.

18F]FDG-PET/CT in adrenal lesions: diagnostic performance in different clinical settings.

Data regarding [18F]FDG-PET/CT for the characterization of adrenal lesions are limited. Most of the studies proposed the tumor-to-liver maximum standardized uptake values (SUVratio) > 1.5 as the best cut off to predict malignancy. The aim of the study was to calculate the optimum cut off in a heterogeneous population with adrenal lesions and evaluate the diagnostic performance SUVratio >1.5. Retrospective analysis of adrenal lesions undergoing [18F]FDG-PET/CT (2013-2022) for different reasons (atypical adrenal incidentalomas, extra adrenal tumor staging). The diagnosis of benignity was assessed by: (i) histology; (ii) stability or minimal diameter increase (<20%/<5 mm) on 12-months follow-up for non-operated patients. The optimal SUVratio and performance of SUVratio >1.5 were calculated by ROC curves. Forty-two consecutive lesions (diameter 36.1 ± 20.3 mm, 6 bilateral) underwent [18F]FDG-PET/CT (19F, age 61.2 ± 11.7 years). Twenty-nine lesions were benign, 11 malignant [8 metastases (2 bilateral) and 1 adrenocortical carcinoma (ACC)] and 2 pheochromocytomas. The SUVratio cut-off in our population was 1.55 (Sn 100%, Sp 73.7%, AUC 0.868), with similar values excluding pheochromocytomas and metastases (SUVratio cut-off 1.49, Sn 100%, Sp 96.3%, AUC 0.988). The SUVratio cut-off of 1.5 showed 100% Sn, 87% Sp, 73% PPV, and 100% NPV. [18F]FDG-PET/CT could help in decision making process avoiding unnecessary surgery. The SUVratio cut-off of 1.5 has a good performance in a heterogenous population.

Integrated testability modeling method of complex systems for fault feature selection and diagnosis strategy optimization

The efficacy of system fault diagnosis is intricately linked with the testability design of the system, particularly in intricate systems encompassing numerous components exhibiting diverse failure modes. However, current research shows a dearth of effective testability models and algorithms for generating diagnostic strategies in multi-valued attribute systems (MVAS) with uncertainty. To address this, the present paper introduces a novel method for testability modeling for complex systems. This approach incorporates signal features in lieu of raw sensor signals within the testability modeling process and accounts for the uncertainties surrounding test outcomes. The context of complex MVAS, characterized by inherent uncertainty, the paper proposes a novel method for constructing a four-value dependency matrix (D-matrix). Furthermore, the paper presents a novel sequential diagnosis strategy optimization approach based on a heuristic evaluation function for the multivalued D-matrix with uncertainty. The proposed methodology has been rigorously validated using a real-world case study involving an aero-engine fuel metering device system. Comparative experiments show that the proposed method can achieve better diagnostic performance in the shortest time.

The Simultaneous Use of Bladder Epicheck® and Urinary Cytology Can Improve the Sensitivity and Specificity of Diagnostic Follow-Up of Urothelial Lesions: Up-to-Date Data from a Multi-Institutional Cohort

Background/Objectives: Bladder cancer is a prevalent urinary system malignancy and urinary cytology is widely used for its screening and follow-up. A novel diagnostic tool called Bladder Epicheck® (BE) is increasingly being used for monitoring the recurrence of non-muscle-invasive bladder cancer (NMIBC). The simultaneous use of BE and urinary cytology can increase the diagnostic performances in the follow-up of bladder neoplasms. Methods: In this multicenter study, we retrospectively evaluated the data of 322 patients in follow-up for a high-grade bladder carcinoma over a six-year period (from January 2018 to March 2024). The diagnostic performances of both cytology and BE and their combination were calculated using histology as gold standard. Results: Recurrences were diagnosed as high-grade urothelial carcinoma NMIBC in 18 cases, low-grade papillary NMIBC in 8 cases, and carcinoma in situ (CIS) in 4 cases. Cytological analysis correctly identified 26 out of 30 carcinomas, while 286 were correctly diagnosed as negative results. BE correctly identified 25 out of 30 carcinomas, 285 were correctly diagnosed as negative results. The combination of BE and urinary cytology correctly identified 29 out of 30 carcinomas, while 289 were correctly diagnosed as negative results. Conclusions: The combination of BE and cytology could be the most effective approach for follow-up diagnosis in patients with high-grade NMIBC, reducing unnecessary invasive procedures.

In silico approach uncovers the shared genetic landscape of type 2 diabetes mellitus and asthenozoospermia

Asthenozoospermia (AZS) is one of the most common types of male infertility. Current evidence revealed that type 2 diabetes mellitus (T2DM) is closely associated with declining semen quality, especially for poor sperm motility. This study aimed to uncover the genetic interrelationships and important biomarkers between AZS and T2DM. Transcriptome data regarding AZS and T2DM were downloaded from the Gene Expression Omnibus (GEO) database. We performed GO and pathway analysis, and protein-protein interaction (PPI) network construction for T2DM-related differentially expressed genes (DMRGs). Moreover, we calculated receiver operator characteristic (ROC) curve and conducted external independent validation. Expression of hub DMRGs was assessed for patients using the qPCR method. MiRNA interaction and immune infiltration were subsequently characterized. A total of 554 overlapping DMRGs were identified between the AZS/T2DM and healthy groups. These overlapping DMRG participated in the DNA damage-, energy metabolism-, and immune-related biological pathways. Module function analysis discovered that the top three PPI modules were tightly correlated with DNA damage-related processes. After external validation in other independent datasets, two hub DMRGs (TBC1D12 and SCG5) were obtained. ROC analysis revealed that TBC1D12 and SCG5 had good diagnostic performance (area under the curve > 0.75). Immune infiltration profile showed that the level of T cell co-stimulation and CD8+_T_cells were negatively related to the hub DMRGs expression. Mirna interaction analysis showed 15 significant hub DMRGs-miRNA interactions. The qPCR results showed that expression of TBC1D12 and SCG5 were significantly different between sperm samples from diabetic patients with AZS and controls. The present study revealed molecular signatures and critical pathways between the AZS and T2DM, and identified two hub DMRGs of TBC1D12 and SCG5. The data would provide novel understandings of shared pathogenic mechanisms in T2DM-associated AZS.

Comparative evaluation of plasma biomarkers of Schistosoma haematobium infection in endemic populations from Burkina Faso.

Infection with Schistosoma haematobium causes urogenital disease associated with organ disfunction, bleeding, pain, and higher susceptibility to infections and cancer. Timely and accurate diagnosis is crucial for prompt and appropriate treatment as well as surveillance efforts, and the use of plasma biomarkers offers important advantages over parasitological examination of urine, including increased sensitivity and the possibility to use the same specimen for multiple investigations. The present study aims to evaluate the diagnostic performance of different plasma biomarkers in endemic populations from Burkina Faso, West Africa. Schistosoma spp. Circulating Anodic Antigen (CAA), cell free S. haematobium DNA (cfDNA), class M and G antibodies against S. haematobium Soluble Worm Antigen Preparation (SWAP) and Soluble Egg Antigen (SEA) were measured in 406 plasma samples. Results of each biomarker test were compared to those of CAA, a Composite Reference Standard (CRS) and Latent Class Analysis (LCA). An identical proportion of positive samples (29%) was observed as a result of CAA and cfDNA testing, with a substantial agreement (84%, Cohen k = 0.62) between the results of the two tests, and a comparable agreement with the results of CRS and LCA. A higher positivity was observed, as expected, as a result of specific antibody testing (47%-72%), with IgG showing a higher agreement than IgM with the three references. Also, higher IgG levels were observed in current vs past infection, and ROC analysis identified optimal cutoff values for improved testing accuracy. This study provides compelling evidence that can inform the choice of the most appropriate diagnostic plasma biomarker for urogenital schistosomiasis in endemic areas, depending on the purpose, context, and available resources for testing. Either CAA or cfDNA testing can be used for the diagnosis of patients and for epidemiological investigations, even in absence of urine filtration microscopy, whereas anti-SWAP or anti-SEA IgG can be employed for surveillance and integrated monitoring of control interventions against poverty-associated diseases.

Prospective Evaluation of Artificial Intelligence Triage of Incidental Pulmonary Emboli on Contrast-Enhanced CT Examinations of the Chest or Abdomen.

BACKGROUND. Artificial intelligence (AI) algorithms improved detection of incidental pulmonary embolism (IPE) on contrast-enhanced CT (CECT) examinations in retrospective studies; however, prospective validation studies are lacking. OBJECTIVE. The purpose of this study was to assess the effect on radiologists' real-world diagnostic performance and report turnaround times of a radiology department's clinical implementation of an AI triage system for detecting IPE on CECT examinations of the chest or abdomen. METHODS. This prospective single-center study included consecutive adult patients who underwent CECT of the chest or abdomen for reasons other than pulmonary embolism (PE) detection from May 12, 2021, to June 30, 2021 (phase 1), or from September 30, 2021, to December 4, 2021 (phase 2). Before phase 1, the radiology department installed a commercially available AI triage algorithm for IPE detection that automatically processed CT examinations and notified radiologists of positive results through an interactive floating widget. In phase 1, the widget was inactive, and radiologists interpreted examinations without AI assistance. In phase 2, the widget was activated, and radiologists interpreted examinations with AI assistance. A review process involving a panel of radiologists was implemented to establish the reference standard for the presence of IPE. Diagnostic performance and report turnaround times were compared using the Pearson chi-square test and Wilcoxon rank sum test, respectively. RESULTS. Phase 1 included 1467 examinations in 1434 patients (mean age, 53.8 ± 18.5 [SD] years; 753 men, 681 women); phase 2 included 3182 examinations in 2886 patients (mean age, 55.4 ± 18.2 years; 1520 men, 1366 women). The frequency of IPE was 1.4% (20/1467) in phase 1 and 1.6% (52/3182) in phase 2. Radiologists without AI, in comparison to radiologists with AI, showed significantly lower sensitivity (80.0% vs 96.2%, respectively; p = .03), without a significant difference in specificity (99.9% vs 99.9%, p = .58), for the detection of IPE. The mean report turnaround time for IPE-positive examinations was not significantly different between radiologists without AI and radiologists with AI (78.3 vs 74.6 minutes, p = .26). CONCLUSION. An AI triage system improved radiologists' sensitivity for IPE detection on CECT examinations of the chest or abdomen without significant change in report turnaround times. CLINICAL IMPACT. This prospective real-world study supports the use of AI assistance for maximizing IPE detection.

The development of a machine learning model to train junior ophthalmologists in diagnosing the pre-clinical keratoconus

PurposeThis study aims to evaluate the diagnostic performance of a machine learning model (ML model) to train junior ophthalmologists in detecting preclinical keratoconus (PKC).MethodsA total of 1,334 corneal topography images (The Pentacam HR system) from 413 keratoconus eyes, 32 PKC eyes and 222 normal eyes were collected. Five junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the ML model. The diagnostic performance of PKC was evaluated among three groups: junior ophthalmologist group (control group), ML model group and ML model-training junior ophthalmologist group (test group).ResultsThe accuracy of the ML model between the eyes of patients with KC and NEs in all three clinics (99% accuracy, area under the receiver operating characteristic (ROC) curve AUC of 1.00, 99% sensitivity, 99% specificity) was higher than that for Belin-Ambrósio enhanced ectasia display total deviation (BAD-D) (86% accuracy, AUC of 0.97, 97% sensitivity, 69% specificity). The accuracy of the ML model between eyes with PKC and NEs in all three clinics (98% accuracy, AUC of 0.96, 98% sensitivity, 98% specificity) was higher than that of BAD-D (69% accuracy, AUC of 0.73, 67% sensitivity, 69% specificity). The diagnostic accuracy of PKC was 47.5% (95%CI, 0.5–71.6%), 100% (95%CI, 100–100%) and 94.4% (95%CI, 14.7–94.7%) in the control group, ML model group and test group. With the assistance of the proposed ML model, the diagnostic accuracy of junior ophthalmologists improved with statistical significance (p &amp;lt; 0.05). According to the questionnaire of all the junior ophthalmologists, the average score was 4 (total 5) regarding to the comprehensiveness that the AI model has been in their keratoconus diagnosis learning; the average score was 4.4 (total 5) regarding to the convenience that the AI model has been in their keratoconus diagnosis learning.ConclusionThe proposed ML model provided a novel approach for the detection of PKC with high diagnostic accuracy and assisted to improve the performance of junior ophthalmologists, resulting especially in reducing the risk of missed diagnoses.

MRI diagnostic performance and strategies for identifying retears in treated pediatric discoid and non-discoid lateral meniscal repairs.

Magnetic resonance imaging (MRI) plays a role in assessing retears of previously treated lateral menisci (LM). We aim to characterize the rate and pattern of arthroscopically confirmed LM retears in children with and without discoid morphology. 288 patients (312 knees) who underwent arthroscopic repair of isolated LM tears between 2018 and 2023 were identified. 20 patients (21 knees) had arthroscopically confirmed isolated LM retear. This included 11 retears of discoid lateral meniscus (DLM) and 10 retears of non-DLM. MRI correctly identified retear in 81.8% of DLM cases (n = 9) and 100% of non-DLM cases (overall 90.5%, P = 0.476). 78% of DLM cases (n = 7) had residual discoid elements. MRI is highly accurate in diagnosing retears in isolated LM injuries (90.5%). We suggest that the radiological approach to assessing meniscal retears can be similarly applied to treated discoid and non-discoid menisci. However, special attention to residual discoid elements is crucial in previously treated DLMs.

Deep learning-assisted distinguishing breast phyllodes tumors from fibroadenomas based on ultrasound images: a diagnostic study.

To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumors (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences. We retrospectively collected 1180 ultrasound images from 539 patients (247 PTs and 292 FAs). Five DL network models with different structures were trained and validated using nodule regions annotated by radiologists on breast ultrasound images. DL models were trained using the methods of transfer learning and 3-fold cross-validation. The model demonstrated the best evaluation index in the 3-fold cross-validation was selected for comparison with radiologists' diagnostic decisions. Two-round reader studies were conducted to investigate the value of DL model in assisting six radiologists with different levels of experience. Upon testing, Xception model demonstrated the best diagnostic performance (AUC: 0.87, 95%CI: 0.81-0.92), outperforming all radiologists (all p < 0.05). Additionally, the DL model enhanced the diagnostic performance of radiologists. Accuracy demonstrated improvements of 4%, 4%, and 3% for senior, intermediate, and junior radiologists, respectively. The DL models showed superior predictive abilities compared to experienced radiologists in distinguishing breast PTs from FAs. Utilizing the model led to improved efficiency and diagnostic performance for radiologists with different levels of experience (6-25 years of work). We developed and validated a DL model based on the largest available dataset to assist in diagnosing PTs. This model has the potential to allow radiologists to discriminate two types of breast tumors which are challenging to identify with precision and accuracy, and subsequently to make more informed decisions about surgical plans.