Diseased Cases Research Articles

SoftLungX: leveraging transfer learning with convolutional neural networks for accurate respiratory disease classification in chest X-ray images

Medical imaging is an indispensable and very important step in the diagnosis and treatment of illnesses. However, due to large amounts of resources necessary for training the model, training from scratch may not invariably emerge as the optimal recourse. Transfer learning has emerged as a viable solution, where pretrained weights from ImageNet are initially utilized and fine-tuninned afterwards. This paper presents a novel approach employing transfer learning to classify both respiratory diseases and radiological findings from chest X-rays. The dataset comprises 191 660 X-ray images gathered from public databases and 752 chest X-ray images obtained from a retrospective study at the University Clinical Center of Kragujevac, forming the foundation of Big data analysis. It includes various conditions such as atelectasis, cardiomegaly, infiltration, pleural thickening, non-viral pneumonia, pneumothorax, COVID-19 viral pneumonia, tuberculosis, etc. as well as masses and nodules indicative of tumors and images of healthy subjects. Although there are existing solutions with some diseases, this is the first paper that inlcludes differentiation between healthy and diseased cases, as well as in case of present disease, as much as 18 different classes are inlcuded in the analysis, which has not been done so far. The proposed methodology included transfer learning with DenseNet121 as the backbone and CheXNeXt weights as initialisation scheme, upon which additional layers were added for fine tuning. The results demonstrate the model’s capacity to distinguish between healthy and diseased, with an average area under curve (AUC) of 0.99. The AUC ranges from 0.86 for nodules to 0.99 for pneumonia and 0.99 for COVID-19. Our method outperforms the state-of-the-art in accuracy across all 18 classes, with the exception of classes consolidation, mass, and nodule. These findings are promising for a tested clinical site, though further validation in clinical environment accros multiple sites may be necessary before using the model as a standalone decision system, rather than a decision support system for disease diagnosis based on chest X-rays.

Predicting acetabular version in native hip joints through plain x-ray radiographs: a comparative analysis of convolutional neural network model and the current gold standard, with insights and implications for hip arthroplasty.

This study presents the development and validation of a Deep Learning Convolutional Neural Network (CNN) model for estimating acetabular version (AV) from native hip plain radiographs. Utilizing a dataset comprising 300 participants with unrelated pelvic complaints, the CNN model was trained and evaluated against CT-Scans, considered the gold standard, using a 5-fold cross-validation. Notably, the CNN model exhibited a robust performance, demonstrating a strong Pearson correlation with CT-Scans (right hip: r = 0.70, p < 0.001; left hip: r = 0.71, p < 0.001) and achieving a mean absolute error of 2.95°. Remarkably, over 83% of predictions yielded errors ≤5°, highlighting the model's high precision in AV estimation. The model holds promise in preoperative planning for hip arthroplasty, potentially reducing complications like recurrent dislocation and component wear. Future directions include further refinement of the CNN model, with ongoing investigations aimed at enhancing preoperative planning potential and ensuring comprehensive assessment across diverse patient populations, particularly in diseased cases. Additionally, future research could explore the model's potential value in scenarios necessitating minimized ionizing radiation exposure, such as post-operative evaluations.

Capturing biomarkers associated with Alzheimer disease subtypes using data distribution characteristics.

Late-onset Alzheimer disease (AD) is a highly complex disease with multiple subtypes, as demonstrated by its disparate risk factors, pathological manifestations, and clinical traits. Discovery of biomarkers to diagnose specific AD subtypes is a key step towards understanding biological mechanisms underlying this enigmatic disease, generating candidate drug targets, and selecting participants for drug trials. Popular statistical methods for evaluating candidate biomarkers, fold change (FC) and area under the receiver operating characteristic curve (AUC), were designed for homogeneous data and we demonstrate the inherent weaknesses of these approaches when used to evaluate subtypes representing less than half of the diseased cases. We introduce a unique evaluation metric that is based on the distribution of the values, rather than the magnitude of the values, to identify analytes that are associated with a subset of the diseased cases, thereby revealing potential biomarkers for subtypes. Our approach, Bimodality Coefficient Difference (BCD), computes the difference between the degrees of bimodality for the cases and controls. We demonstrate the effectiveness of our approach with large-scale synthetic data trials containing nearly perfect subtypes. In order to reveal novel AD biomarkers for heterogeneous subtypes, we applied BCD to gene expression data for 8,650 genes for 176 AD cases and 187 controls. Our results confirm the utility of BCD for identifying subtypes of heterogeneous diseases.

Verification Study of in Silico Computed Intracardiac Blood Flow With 4D Flow MRI.

Major challenges for clinical applications of in silico medicine are limitations in time and computational resources. Computational approaches should therefore be tailored to specific applications with relatively low complexity and must be verified and validated against clinical gold standards. This study performed computational fluid dynamics simulations of left ventricular hemodynamics of different complexity based on shape reconstruction from steady state gradient echo magnetic resonance imaging (MRI) data. Computed flow results of a rigid wall model (RWM) and a prescribed motion fluid-structure interaction (PM-FSI) model were compared against phase-contrast MRI measurements for three healthy subjects. Extracted boundary conditions from the steady state MRI sequences as well as computed metrics, such as flow rate, valve velocities, and kinetic energy show good agreement with in vivo flow measurements. Regional flow analysis reveals larger differences. Basic flow structures are well captured with RWM and PM-FSI. For the computation of further biomarkers like washout or flow efficiency, usage of PM-FSI is required. Regarding boundary-near flow, more accurate anatomical models are inevitable. These results delineate areas of application of both methods and lay a foundation for larger validation studies and sensitivity analysis for healthy and diseased cases, being an essential step upon clinical translations.

An assessment of chromosomal alterations detected by FISH in urothelial carcinoma and its correlation to liquid-based urine cytology and histopathology

BackgroundThe present study aimed to evaluate the diagnostic usefulness, sensitivity, and specificity of urinary liquid-based cytology (LBC) versus multitarget, multicolor UroVysion fluorescence in situ hybridization (uFISH) assay for the diagnosis of urinary bladder cancer in light of the histological diagnosis. MethodsFifty patients and negative controls each were selected for the study. A 10 ml voided urine specimen was processed for urine cytology to prepare LBC smears. For urinary FISH analysis, 50 ml of voided urine was processed using multi-target FISH-containing probes for the centromeres of chromosomes 3, 7, and 17 and the 9p21 locus. Histopathological examination of transurethral resection of bladder tumor (TURBT) chips was performed on diseased cases only. ResultsAll 50 diseased cases exhibited 62%, 80%, 76%, and 68% of uFISH sensitivity, uFISH specificity, positive predictive value (PPV) and negative predictive value (NPV) respectively. Among negative controls, results of 40%, 84%, 71%, and 58% for LBC sensitivity, LBC specificity, PPV, and NPV were found, respectively. Among the diseased cases uFISH positivity of 20%, 45%, 83.3%, 82.4% and 100% for Ta, T1, T2a, T2b and T3 respectively. Among the diseased cases LBC positivity was 0%, 20%, 33.3%, 70% and 100% for Ta, T1, T2a, T2b and T3 respectively. ConclusionIncreased sensitivity and specificity of uFISH than LBC urine to detect urothelial carcinoma (UC) reflect the better potentiality of the former to detect UCs. The limitations of uFISH analysis include sophisticated equipment, higher cost, and experienced cytopathologists/technicians. It is proposed to conduct more such prospective trials with recent biomarkers so as to reduce the frequency of cystoscopy in such patients.

A capture-recapture modeling framework emphasizing expert opinion in disease surveillance.

In disease surveillance, capture-recapture methods are commonly used to estimate the number of diseased cases in a defined target population. Since the number of cases never identified by any surveillance system cannot be observed, estimation of the case count typically requires at least one crucial assumption about the dependency between surveillance systems. However, such assumptions are generally unverifiable based on the observed data alone. In this paper, we advocate a modeling framework hinging on the choice of a key population-level parameter that reflects dependencies among surveillance streams. With the key dependency parameter as the focus, the proposed method offers the benefits of (a) incorporating expert opinion in the spirit of prior information to guide estimation; (b) providing accessible bias corrections, and (c) leveraging an adapted credible interval approach to facilitate inference. We apply the proposed framework to two real human immunodeficiency virus surveillance datasets exhibiting three-stream and four-stream capture-recapture-based case count estimation. Our approach enables estimation of the number of human immunodeficiency virus positive cases for both examples, under realistic assumptions that are under the investigator's control and can be readily interpreted. The proposed framework also permits principled uncertainty analyses through which a user can acknowledge their level of confidence in assumptions made about the key non-identifiable dependency parameter.

Systemic inflammation in early lactation and its relation to the cows' oxidative and metabolic status, productive and reproductive performance, and activity

A dysregulated inflammatory response contributes to the occurrence of disorders in cows during the transition period from pregnancy to lactation. However, a detailed characterization of clinically healthy cows that exhibit an enhanced inflammatory response during this critical period remains incomplete. In this experiment, a total of 99 individual transition dairy cows and 109 observations (18 cows monitored in 2 consecutive lactations), submitted to similar transition management were involved to evaluate the relationship between elevated an inflammatory response and metabolic and oxidative status, as well as transition outcomes. Blood was taken at -7, 3, 6, 9, and 21 DIM, and concentrations of metabolic parameters (glucose, β-hydroxybutyric acid, nonesterified fatty acids [NEFA], insulin, IGF-1, and fructosamine) were analyzed. Additionally, oxidative parameters (proportion of oxidized glutathione to total glutathione in red blood cells, the activity of glutathione peroxidase [GPx] and superoxide dismutase, concentrations of malondialdehyde, and oxygen radical absorbance capacity) and acute phase proteins (APP) including haptoglobin (Hp), serum amyloid A (SAA) and albumin-to-globulin ratio (A:G) were determined in the blood at 21 DIM. The 3 APP parameters were used to group clinically healthy cows into 2 categories through k-medoids clustering (i.e., a group showing an acute phase response, APR; n = 39) and a group not showing such a response (i.e., non-APR; n = 50). Diseased cases (n = 20) were handled in a separate group. Lower SAA and Hp concentrations as well as higher A:G were observed in the non-APR group, although for Hp, differences were observed from the APR group and not from the diseased group. Only 1 of the 5 oxidative parameters differed between the groups, with the non-APR group exhibiting lower GPx activity compared with the diseased group. The non-APR group showed the highest IGF-1 levels among the 3 groups and and lower NEFA concentrations compared with the diseased groups. Cows in the diseased group also showed reduced dry matter intake and milk yield compared with clinically healthy cows, regardless of their inflammatory status. Moreover, the APR group exhibited temporarily lower activity levels compared with the non-APR group. These findings highlight that cows with a lower inflammatory status after 21 DIM exhibited better metabolic health characteristics and productive performance, as well as activity levels. Nevertheless, the detrimental effects of a higher inflammatory status in the absence of clinical symptoms are still relatively limited.

A lattice Boltzmann approach to mathematical modeling of myocardial perfusion.

A mathematical model of myocardial perfusion based on the lattice Boltzmann method (LBM) is proposed and its applicability is investigated in both healthy and diseased cases. The myocardium is conceptualized as a porous material in which the transport and mass transfer of a contrast agent in blood flow is studied. The results of myocardial perfusion obtained using LBM in 1D and 2D are confronted with previously reported results in the literature and the results obtained using the mixed-hybrid finite element method. Since LBM is not suitable for simulating flow in heterogeneous porous media, a simplified and computationally efficient 1D-analog approach to 2D diseased case is proposed and its applicability discussed.

Pleiotropic Bias and Study Design Considerations in Genetic Association Studies.

Case-control studies are efficient designs for investigating gene-disease associations. A discovery of genome-wide association studies (GWAS) is that many genetic variants are associated with multiple health outcomes and diseases, a phenomenon known as pleiotropy. We aimed to discuss about pleiotropic bias in genetic association studies. The opinions of the researchers on the basis of the literature were presented as a critical review. Pleiotropic effect can bias the results of gene-disease association studies if they use individuals with pre-existing diseases as the control group, while the disease in cases and controls have shared genetic markers. The idea supports the conclusion that when the exposure of interest in a case-control study is a genetic marker, the use of controls from diseased cases that share similar genetic markers may increase the risk of pleiotropic effect. However, not manifesting the disease symptoms among controls at the time of recruitment does not guarantee that the individual will not develop the disease of interest in the future. Age-matched disease-free controls may be a better solution in similar situations. Different analytical techniques are also available that can be used to identify pleiotropic effects. Known pleiotropic effects can be searched from various online databases. Pleiotropic effects may result in bias in genetic association studies. Suggestions consist of selecting healthy yet age-matched controls and considering diseases with independent genetic architecture. Checking the related databases is recommended before designing a study.

An alternative parameterization for the binormal ROC curve, with applications to sizing and simulation studies.

Because the conventional binormal ROC curve parameters are in terms of the underlying normal diseased and nondiseased rating distributions, transformations of these values are required for the user to understand what the corresponding ROC curve looks like in terms of its shape and size. In this paper I propose an alternative parameterization in terms of parameters that explicitly describe the shape and size of the ROC curve. The proposed two parameters are the mean-to-sigma ratio and the familiar area under the ROC curve (AUC), which are easily interpreted in terms of the shape and size of the ROC curve, respectively. In addition, the mean-to-sigma ratio describes the degree of improperness of the ROC curve and the AUC describes the ability of the corresponding diagnostic test to discriminate between diseased and nondiseased cases. The proposed parameterization simplifies the sizing of diagnostic studies when conjectured variance components are used and simplifies choosing the binormal a and b parameter values needed for simulation studies.

Automatic Diagnosis of Carotid Atherosclerosis Using a Portable Freehand 3-D Ultrasound Imaging System.

The objective of this study is to develop a deep-learning-based detection and diagnosis technique for carotid atherosclerosis (CA) using a portable freehand 3-D ultrasound (US) imaging system. A total of 127 3-D carotid artery scans were acquired using a portable 3-D US system, which consisted of a handheld US scanner and an electromagnetic (EM) tracking system. A U-Net segmentation network was first applied to extract the carotid artery on 2-D transverse frame, and then, a novel 3-D reconstruction algorithm using fast dot projection (FDP) method with position regularization was proposed to reconstruct the carotid artery volume. Furthermore, a convolutional neural network (CNN) was used to classify healthy and diseased cases qualitatively. Three-dimensional volume analysis methods, including longitudinal image acquisition and stenosis grade measurement, were developed to obtain the clinical metrics quantitatively. The proposed system achieved a sensitivity of 0.71, a specificity of 0.85, and an accuracy of 0.80 for diagnosis of CA. The automatically measured stenosis grade illustrated a good correlation ( r = 0.76) with the experienced expert measurement. The developed technique based on 3-D US imaging can be applied to the automatic diagnosis of CA. The proposed deep-learning-based technique was specially designed for a portable 3-D freehand US system, which can provide a more convenient CA examination and decrease the dependence on the clinician's experience.

NORHA: A NORmal Hippocampal Asymmetry Deviation Index Based on One-Class Novelty Detection and 3D Shape Features.

Radiologists routinely analyze hippocampal asymmetries in magnetic resonance (MR) images as a biomarker for neurodegenerative conditions like epilepsy and Alzheimer's Disease. However, current clinical tools rely on either subjective evaluations, basic volume measurements, or disease-specific models that fail to capture more complex differences in normal shape. In this paper, we overcome these limitations by introducing NORHA, a novel NORmal Hippocampal Asymmetry deviation index that uses machine learning novelty detection to objectively quantify it from MR scans. NORHA is based on a One-Class Support Vector Machine model learned from a set of morphological features extracted from automatically segmented hippocampi of healthy subjects. Hence, in test time, the model automatically measures how far a new unseen sample falls with respect to the feature space of normal individuals. This avoids biases produced by standard classification models, which require being trained using diseased cases and therefore learning to characterize changes produced only by the ones. We evaluated our new index in multiple clinical use cases using public and private MRI datasets comprising control individuals and subjects with different levels of dementia or epilepsy. The index reported high values for subjects with unilateral atrophies and remained low for controls or individuals with mild or severe symmetric bilateral changes. It also showed high AUC values for discriminating individuals with hippocampal sclerosis, further emphasizing its ability to characterize unilateral abnormalities. Finally, a positive correlation between NORHA and the functional cognitive test CDR-SB was observed, highlighting its promising application as a biomarker for dementia.

Two-stage multi-task deep learning framework for simultaneous pelvic bone segmentation and landmark detection from CT images.

Pelvic bone segmentation and landmark definition from computed tomography (CT) images are prerequisite steps for the preoperative planning of total hip arthroplasty. In clinical applications, the diseased pelvic anatomy usually degrades the accuracies of bone segmentation and landmark detection, leading to improper surgery planning and potential operative complications. This work proposes a two-stage multi-task algorithm to improve the accuracy of pelvic bone segmentation and landmark detection, especially for the diseased cases. The two-stage framework uses a coarse-to-fine strategy which first conducts global-scale bone segmentation and landmark detection and then focuses on the important local region to further refine the accuracy. For the global stage, a dual-task network is designed to share the common features between the segmentation and detection tasks, so that the two tasks mutually reinforce each other's performance. For the local-scale segmentation, an edge-enhanced dual-task network is designed for simultaneous bone segmentation and edge detection, leading to the more accurate delineation of the acetabulum boundary. This method was evaluated via threefold cross-validation based on 81 CT images (including 31 diseased and 50 healthy cases). The first stage achieved DSC scores of 0.94, 0.97, and 0.97 for the sacrum, left and right hips, respectively, and an average distance error of 3.24mm for the bone landmarks. The second stage further improved the DSC of the acetabulum by 5.42%, and this accuracy outperforms the state-of-the-arts (SOTA) methods by 0.63%. Our method also accurately segmented the diseased acetabulum boundaries. The entire workflow took ~ 10s, which was only half of the U-Net run time. Using the multi-task networks and the coarse-to-fine strategy, this method achieved more accurate bone segmentation and landmark detection than the SOTA method, especially for diseased hip images. Our work contributes to accurate and rapid design of acetabular cup prostheses.

Effect of external magnetic field on realistic bifurcated right coronary artery hemodynamics

Diagnostic technology based on magnetic fields is commonly used in medicine for diagnosis and therapy. However, the exposure to strong electromagnetic fields has adverse outcomes in patients. Thus, the objective of the current study is to investigate the effect of applying external uniform magnetic fields on the blood flow in both healthy and diseased cases of right coronary artery and determine the safe values of the applied magnetic field strengths. The diseased cases include a 40% stenosed artery along with two blood disorder cases with a hematocrit level of 20% and 60%. A comprehensive three-dimensional steady non-Newtonian flow model is developed using the Casson model to investigate the effect of the magnetic field on both shear rate and hematocrits. The model is numerically simulated at different values of magnetic field strengths and its orientation. The results indicated that the magnetic field in the Y-direction has a dominant effect compared to other directions. Moreover, the maximum increase in the main branch mass flow rate fraction is about 6.2%. Another interesting finding is that the wall shear stress is slightly affected by the magnetic field strength. For the stenosed case, it is found that the high magnetic field strengths can reduce the formulation of the vortices and hence reduce the risk of thrombosis, which agrees with published works. Additionally, the obtained results confirm that using a magnetic field up to 11.7 T, which is used in magnetic resonance imaging devices, is safe, and has a slight effect on blood flow parameters such as the wall shear stress.

Mannose-binding lectin gene 2 variant DD (rs 5030737) is associated with susceptibility to COVID-19 infection in the urban population of Patna City (India).

The study aimed to measure plasma levels of Mannose-Binding Lectin (MBL) and MBL-associated serine protease-2 (MASP-2) and theirpolymorphisms in COVID-19 patients and controls to detect association. As MBL is a protein of immunological importance, it may contribute to the first-line host defence against SARS-CoV-2. MBL initiates the lectin pathway of complement activation with help of MASP-1 and MASP-2. Hence, appropriate serum levels of MBL and MASPs are crucial in getting protection from the disease. The polymorphisms of MBL and MASP genes affect their plasma levels, impacting their protective function and thus may manifest susceptibility, extreme variability in the clinical symptoms and progression of COVID-19 disease. The present study was conducted to find plasma levels and genetic variations in MBL and MASP-2 in COVID-19 patients and controls using PCR-RFLP and ELISA, respectively.The present study was conducted to find plasma levels and genetic variations in MBL and MASP-2 in COVID-19 patients and controls using PCR-RFLP and ELISA, respectively. Our results indicate that median serum levels of MBL and MASP-2 were significantly low in diseased cases but attained normal levels on recovery. Only genotypeDD was found to be associated with COVID-19 cases in the urban population of Patna city.

AUTOMATED SEVERITY SCORING OF COVID-19 CT SEQUENCES USING SPACE-TIME TRANSFORMERS

Automatic diagnosis of Covid-19 lung complications from Computerized Tomography (CT) scans is an increasingly important research topic. In this rapidly developing area of Covid detection from medical image sequences, it is noted that most prior literature has focused on binary classification to detect diseased versus healthy cases from single X-ray or CT image. In this paper, we advance a step further by presenting a comprehensive framework for automated classification of the severity of lung infection (mild, moderate and severe) from CT sequences of confirmed Covid cases. We consider the sequence information for automation because in practice, the medical experts look at the CT sequence to score the severity of infection. We have collected a new lung CT sequence dataset at various stages of Covid infection from Indian patients. This dataset has been scored in terms of the severity of each lung lobe by experts in the field. We present a novel application of space-time transformers for CT sequences and achieve 93.3% accuracy for sequence level and 99% accuracy for patient-level, for multi- class classification of severity classes.

Structure, Process and Outcome Evaluation of Central Public Health Laboratory Performance About Neonatal Screening Program in Karbala in 2022

Background: Neonatal screening is a preventive medical act aims to early detecting neonates affected byspecific disorders that endanger their lives or cause long-term health problems and require prompt intervention. This canresult in significant reductions in morbidity, death, and related costs and disabilities. The panel of neonatal screened disordershas been gradually expanded. Expansion has been empowered with the introduction of tandem mass spectrometry, whichallowed for the screening of up to 40–50 diseases with a single blood spot. Part of the implementation of neonatal screeningprogram must include, just like any other program, monitoring and evaluation task in order to identify difficultiesor barriers to improve procedures and methodologies. This study aimed to evaluate the Central Public Health Laboratory/Karbala Directorate of Health performance in Neonatal Screening program. Method: The evaluation method has beencarried out in Holy Karbala Governorate for the period between the first of September 2021 and the end of June 2022using a check list of standards taken from the National Guideline for Newborn Screening for Care Providers inPrimary Health Care Centers in Iraq. This was to evaluateCentral Public Health Laboratory by meeting the manager employee and evaluate theCentral Public Health Laboratory room.

Expression changes of multiple marker genes and analysis of circulating tumour cells and dehydrogenase for prognostic and pharmacodynamic measures

ObjectivesThe main objectives of the studies are to analyze melanoma markers from the tumour and blood samples. MethodsLactate dehydrogenase (LDH) was assayed from the serum sample to determine metastatic melanoma. The prevalence of circulating tumour cells was explored in tumour subjects and compared with non-tumour cases. Expression analysis of TGF-β and cathepsin-K was determined from the non-melanoma and melanoma cases using western blotting analysis. ResultsThe amount of LDH was elevated in most metastatic patients (p < 0.0001). About 10% of patients were associated with the micrometastatic stage and 16.3% of patients were linked with multiple melanomas. The amount of ctDNA considerably declined at <0.01% level after one month of clinical development. The amount of ctDNA in the blood in an initial stage indicates resistance to specific targeted therapy. Expression of S100 level also decreased after two weeks of treatment. TGF-β showed maximum variation in mRNA expression between diseased and healthy cases, whereas, an increased amount of cathepsin-K biosynthesis was observed in tumour tissues (p < 0.001). ConclusionsMDH, ctDNA, TGF-β and S100 could be potential markers to predict early determination of tumour and non-tumour tissues. Monitoring of ctDNA and S100 is useful to analyze the health benefits of melanoma patients.

Annotated computed tomography coronary angiogram images and associated data of normal and diseased arteries

Computed Tomography Coronary Angiography (CTCA) is a non-invasive method to evaluate coronary artery anatomy and disease. CTCA is ideal for geometry reconstruction to create virtual models of coronary arteries. To our knowledge there is no public dataset that includes centrelines and segmentation of the full coronary tree. We provide anonymized CTCA images, voxel-wise annotations and associated data in the form of centrelines, calcification scores and meshes of the coronary lumen in 20 normal and 20 diseased cases. Images were obtained along with patient information with informed, written consent as part of the Coronary Atlas. Cases were classified as normal (zero calcium score with no signs of stenosis) or diseased (confirmed coronary artery disease). Manual voxel-wise segmentations by three experts were combined using majority voting to generate the final annotations. Provided data can be used for a variety of research purposes, such as 3D printing patient-specific models, development and validation of segmentation algorithms, education and training of medical personnel and in-silico analyses such as testing of medical devices.

Periapical Radiography versus Cone Beam Computed Tomography in Endodontic Disease Detection: A Free-response, Factorial Study

AimTo assess and compare reader performance in interpreting digital periapical (PA) radiography and cone beam computed tomography (CBCT) in endodontic disease detection, using a free-response, factorial model. Materials and MethodsA reader performance study of 2 image test sets was undertaken using a factorial, free-response design, accounting for the independent variables: case type, case severity, reader type, and imaging modality. Twenty-two readers interpreted 60 PA and 60 CBCT images divided into 5 categories: diseased–subtle, diseased–moderate, diseased–obvious, nondiseased–subtle, and nondiseased–obvious. Lesion localization fraction, specificity, false positive (FP) marks, and the weighted alternative free-response receiver operating characteristic figure of merit were calculated. ResultsCBCT had greater specificity than PA in the obvious nondiseased cases (P = .01) and no significant difference in the subtle nondiseased category. Weighted alternative free-response receiver operating characteristic values were higher for PA than CBCT in the subtle diseased (P = .02) and moderate diseased (P = .01) groups with no significant difference between in the obvious diseased groups. CBCT had higher mean FPs than PA (P < .05) in subtle diseased cases. Mean lesion localization fraction in the moderate diseased group was higher in PA than CBCT (P = .003). No relationships were found between clinical experience and all diagnostic performance measures, except for in the obvious diseased CBCT group, where increasing experience was associated mean FP marks (P = .04). ConclusionsReader performance in the detection of endodontic disease is better with PA radiography than CBCT. Clinical experience does not impact upon the accuracy of interpretation of both PA radiography and CBCT.