Sensitivity Of Metrics Research Articles

In vitro to in vivo extrapolation from 3D hiPSC-derived cardiac microtissues and physiologically based pharmacokinetic modeling to inform next-generation arrhythmia risk assessment.

Proarrhythmic cardiotoxicity remains a substantial barrier to drug development as well as a major global health challenge. In vitro human pluripotent stem cell-based new approach methodologies have been increasingly proposed and employed as alternatives to existing in vitro and in vivo models that do not accurately recapitulate human cardiac electrophysiology or cardiotoxicity risk. In this study, we expanded the capacity of our previously established 3D human cardiac microtissue model to perform quantitative risk assessment by combining it with a physiologically based pharmacokinetic model, allowing a direct comparison of potentially harmful concentrations predicted in vitro to in vivo therapeutic levels. This approach enabled the measurement of concentration responses and margins of exposure for 2 physiologically relevant metrics of proarrhythmic risk (i.e. action potential duration and triangulation assessed by optical mapping) across concentrations spanning 3 orders of magnitude. The combination of both metrics enabled accurate proarrhythmic risk assessment of 4 compounds with a range of known proarrhythmic risk profiles (i.e. quinidine, cisapride, ranolazine, and verapamil) and demonstrated close agreement with their known clinical effects. Action potential triangulation was found to be a more sensitive metric for predicting proarrhythmic risk associated with the primary mechanism of concern for pharmaceutical-induced fatal ventricular arrhythmias, delayed cardiac repolarization due to inhibition of the rapid delayed rectifier potassium channel, or hERG channel. This study advances human-induced pluripotent stem cell-based 3D cardiac tissue models as new approach methodologies that enable in vitro proarrhythmic risk assessment with high precision of quantitative metrics for understanding clinically relevant cardiotoxicity.

Utilization of artificial intelligence in minimally invasive right adrenalectomy: recognition of anatomical landmarks with deep learning

Background The primary surgical approach for removing adrenal masses is minimally invasive adrenalectomy. Recognition of anatomical landmarks during surgery is critical for minimizing complications. Artificial intelligence-based tools can be utilized to create real-time navigation systems during laparoscopic and robotic right adrenalectomy. In this study, we aimed to develop deep learning models that can identify critical anatomical structures during minimally invasive right adrenalectomy. Methods In this experimental feasibility study, intraoperative videos of 20 patients who underwent minimally invasive right adrenalectomy in a tertiary care center between 2011 and 2023 were analyzed and used to develop an artificial intelligence-based anatomical landmark recognition system. Semantic segmentation of the liver, the inferior vena cava (IVC), and the right adrenal gland were performed. Fifty random images per patient during the dissection phase were extracted from videos. The experiments on the annotated images were performed on two state-of-the-art segmentation models named SwinUNETR and MedNeXt, which are transformer and convolutional neural network (CNN)-based segmentation architectures, respectively. Two loss function combinations, Dice-Cross Entropy and Dice-Focal Loss were experimented with for both of the models. The dataset was split into training and validation subsets with an 80:20 distribution on a patient basis in a 5-fold cross-validation approach. To introduce a sample variability to the dataset, strong-augmentation techniques were performed using intensity modifications and perspective transformations to represent different surgery environment scenarios. The models were evaluated by Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) which are widely used segmentation metrics. For pixelwise classification performance, accuracy, sensitivity and specificity metrics were calculated on the validation subset. Results Out of 20 videos, 1000 images were extracted, and the anatomical landmarks (liver, IVC, and right adrenal gland) were annotated. Randomly distributed 800 images and 200 images were selected for the training and validation subsets, respectively. Our benchmark results show that the utilization of Dice-Cross Entropy Loss with the transformer-based SwinUNETR model achieved 78.37%, whereas the CNN-based MedNeXt model reached a 77.09% mDSC score. Conversely, MedNeXt reaches a higher mIoU score of 63.71% than SwinUNETR by 62.10% on a three-region prediction task. Conclusion Artificial intelligence-based systems can predict anatomical landmarks with high performance in minimally invasive right adrenalectomy. Such tools can later be used to create real-time navigation systems during surgery in the near future.

Deep learning automatic semantic segmentation of glioblastoma multiforme regions on multimodal magnetic resonance images.

In patients having naïve glioblastoma multiforme (GBM), this study aims to assess the efficacy of Deep Learning algorithms in automating the segmentation of brain magnetic resonance (MR) images to accurately determine 3D masks for 4 distinct regions: enhanced tumor, peritumoral edema, non-enhanced/necrotic tumor, and total tumor. A 3D U-Net neural network algorithm was developed for semantic segmentation of GBM. The training dataset was manually delineated by a group of expert neuroradiologists on MR images from the Brain Tumor Segmentation Challenge 2021 (BraTS2021) image repository, as ground truth labels for diverse glioma (GBM and low-grade glioma) subregions across four MR sequences (T1w, T1w-contrast enhanced, T2w, and FLAIR) in 1251 patients. The in-house test was performed on 50GBM patients from our cohort (PerProGlio project). By exploring various hyperparameters, the network's performance was optimized, and the most optimal parameter configuration was identified. The assessment of the optimized network's performance utilized Dice scores, precision, and sensitivity metrics. Our adaptation of the 3D U-net with additional residual blocks demonstrated reliable performance on both the BraTS2021 dataset and the in-house PerProGlio cohort, employing only T1w-ce sequences for enhancement and non-enhanced/necrotic tumor models and T1w-ce + T2w + FLAIR for peritumoral edema and total tumor. The mean Dice scores (training and test) were 0.89 and 0.75; 0.75 and 0.64; 0.79 and 0.71; and 0.60 and 0.55, for total tumor, edema, enhanced tumor, and non-enhanced/necrotic tumor, respectively. The results underscore the high precision with which our network can effectively segment GBM tumors and their distinct subregions. The level of accuracy achieved agrees with the coefficients recorded in previous GBM studies. In particular, our approach allows model specialization for each of the different tumor subregions employing only those MR sequences that provide value for segmentation.

Artificial Intelligence in Early Detection of Cervical Intraepithelial Neoplasia

Artificial Intelligence (AI) is a quickly evolving field of technology used to develop intelligent machines capable of performing tasks such as problem solving, decision making , perception, language processing, and learning. This paper explores the application of AI in the field of gynecological oncology, specifically in the diagnosis of cervical cancer. The paper proposes a hybrid AI model that uses a Gaussian mixture model and a deep learning model to segment and classifies colposcope images. The model performed with satisfactory segmentation metrics of sensitivity, specificity, dice index, and Jaccard index of 0.976, 0.989, 0.954, and 0.856, respectively. This model aims to accurately classify cancer and non-cancer cases from a colposcope image. The results showed that this method could effectively segment the colposcopy images and extract the cervix region. This can be a valuable tool for automated cancer diagnosis and can help improve the diagnosis's accuracy.

Sensitivity enhancement using triple metal gate work function engineering of junctionless cylindrical gate all around SiNW MOSFET based biosensor for neutral biomolecule species detection for upcoming sub 14 nm technology node

In the present work Dielectric Modulation (DM) technique along with Triple Metal (TM) gate engineering has been used for the junctionless (JL) cylindrical gate all around (CGAA) Si nanowire (NW) MOSFET based biosensor for label free electrical detection of neutral biomolecules species like Uricase, Streptavidin, Protein, Biotin, ChOx (choline oxidase), and APTES etc. For this device, the Drift Diffusion approach has been used along with self-consistent solution of Schrodinger’s equation with Poisson’s equation. For the biomolecule immobilization, a nanogap cavity region is formed in the JL SiNW MOSFET by etching gate oxide layer above the SiO2 interfacial layer. The change in the drain current (ID), threshold voltage (Vth), off-current sensitivity (SIoff), threshold voltage sensitivity (SVth) and Ion/Ioff current ratio of the device have been considered as the sensing parameters for detection of biomolecules under dry environment condition. In present work a new sensing metric of sensing of biomolecules – DIBL has been added which has never been reported in literature. In this work for JL SiNW, sensitivity metrics like smaller DIBL ∼50.47 mV/V, higher SIoff9.33×105, higher SVth28.72, nearly ideal subthreshold slope (SS) ∼60 mV/dec, and higher Ion/Ioff current ratio ∼9.01 × 1012 have been obtained in comparison to available literature results.

Tumor origin identification through machine learning and gene expression profiling.

e13597 Background: Accurate identification of tumor origin is crucial for effective diagnosis and treatment, particularly in cases of metastatic tumors. Interpretable machine learning models have displayed significant potential in addressing this problem when imaging and immunohistochemistry (IHC) examinations are ineffective. In this study, we developed a panel using gene expression profiles from various tumors and constructed a robust machine learning model for precise identification of tumor origin. Methods: RNA sequencing (RNA-seq) data of 9462 tumor samples originating from 21 different organs were collected from The Cancer Genome Atlas (TCGA). We conducted feature engineering through unsupervised clustering and differential gene expression analysis, selecting a refined panel of 164 genes from a pool of over 60,000 identifiers. Subsequently, a machine learning classifier grounded in Logistic regression (LR) was trained on 9462 samples with the constructed 164-gene panel. To enhance the adaptability of our model to this task, 10-fold cross-validation was employed in the multi-class mode. Two independent test sets, the Primary tumor set (PT, n=3420, including 19 tumor types) and the Metastatic tumor set (MTP, n=100, all originating from the prostate and spread to bone, liver, etc.), were established with samples from Gene Expression Omnibus (GEO) and other published studies. Notably, all samples underwent FPKM normalization and there was no overlap between training and testing samples. Model performance was assessed using accuracy, specificity, and sensitivity metrics. Results: The 164-gene expression panel achieved a cross-validation accuracy of 96.73%. In the assessment of the PT test set, our model achieved an overall 99.62% specificity, 92.31% accuracy and 89.79% sensitivity, exhibiting performance comparable to similar models in other studies. Additionally, our model accurately traced 91% of metastatic tumors in the MTP test set to the prostate, surpassing previous lines of work by a large margin. Conclusions: Gene expression patterns reveal organ-specific characteristics that could be used to identify tumor origin. The combination of a condensed yet comprehensive gene expression panel with a robust machine learning model serves as a promising tool for tumor diagnosis. Ongoing correlative studies aim to extend predictions from tissue organs to cancer subtypes.

Differentiating neural sensitivity and bias during face-emotion processing in youth: a computational approach.

The ability to interpret face-emotion displays is critical for the development of adaptive social interactions. Using a novel variant of a computational model and fMRI data, we examined behavioral and neural associations between two metrics of face-emotion labeling (sensitivity and bias) and age in youth. Youth and adults (n = 44, M age = 20.02, s.d. = 7.44, range = 8-36) completed an explicit face-emotion labeling fMRI task including happy to angry morphed face emotions. A drift-diffusion model was applied to choice and reaction time distributions to examine sensitivity and bias in interpreting face emotions. Model fit and reliability of parameters were assessed on adult data (n = 42). Linear and quadratic slopes modeled brain activity associated with dimensions of face-emotion valence and ambiguity during interpretation. Behaviorally, age was associated with sensitivity. The bilateral anterior insula exhibited a more pronounced neural response to ambiguity with older age. Associations between sensitivity and bias metrics and activation patterns indicated that systems encoding face-emotion valence and ambiguity both contribute to the ability to discriminate face emotions. The current study provides evidence for age-related improvement in perceptual sensitivity to facial affect across adolescence and young adulthood.

Proposal for variability-induced effective radius of elliptical gate-all-around junctionless transistors and its applicability in hydrogen gas sensors

Nano-ribbons with gate-all-around (GAA) architecture have been predicted to serve as next-generation on-chip transistors, among which, the junctionless transistor (JLT) has emerged as a promising candidate, mitigating few limitations of conventional inversion-mode metal–oxide–semiconductor field-effect transistors (MOSFETs). Due to imperfect fabrication process, the GAA architecture is often subject to cross-sectional variabilities. This work presents an analytical model for the effective radius of elliptical cross-section of the channel region having a non-circular oxide surrounding it, resulting in various structural configurations having non-uniform oxide thickness. To demonstrate the impact of cross-sectional variability of GAA-JLTs, few performance parameters, such as, drain current, threshold voltage, and so on, have been evaluated using our proposed formulation for the effective radius and the results were found to agree well with TCAD simulations. As a case study, we have emphasized on the applicability of our proposed model in variability-induced GAA-JLT-based hydrogen sensors using few sensitivity metrics. Our computations show substantial changes in the sensing device as the cross-section deviates from the ideal circular one. Our computations reveal that incorporating the effects of radius variability in the device model along with proper tuning of operating conditions can enhance the performance and accuracy of such sensors.

FKeras: A Sensitivity Analysis Tool for Edge Neural Networks

Edge computation often requires robustness to faults, e.g., to reduce the effects of transient errors and to function correctly in high radiation environments. In these cases, the edge device must be designed with fault tolerance as a primary objective. FKeras is a tool that helps design fault-tolerant edge neural networks that run entirely on chip to meet strict latency and resource requirements. FKeras provides metrics that give a bit-level ranking of neural network weights with respect to their sensitivity to faults. FKeras includes these sensitivity metrics to guide efficient fault injection campaigns to help evaluate the robustness of a neural network architecture. We show how to use FKeras in the co-design of edge NNs trained on the high-granularity endcap calorimeter dataset, which represents high energy physics data, as well as the CIFAR-10 dataset. We use FKeras to analyze a NN’s fault tolerance to consider alongside its accuracy, performance, and resource consumption. The results show that the different NN architectures have vastly differing resilience to faults. FKeras can also determine how to protect neural network weights best, e.g., by selectively using triple modular redundancy on only the most sensitive weights, which reduces area without affecting accuracy.

Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis.

Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI's diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists' ability, and explore the main factors affecting AI's performance. The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data. Twelve studies with 11,173 patients were included, demonstrating AI models' efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92-0.96) and specificity of 93% (95% confidence interval: 0.89-0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%). AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.

Comparison of different 2D muscle indexes measured at the level of the 3rd lumbar vertebra in survival prediction in patients with renal cell carcinoma.

Low computed tomography (CT)-determined muscle mass, commonly determined with height-adjusted muscle indexes (MIs), predicts worse survival in several cancers and has been suggested as a prognostic assessment tool. Although several MIs measured at the level of the 3rd lumbar vertebra (L3) are commonly used, it remains unestablished how different L3-determined MIs perform in survival prognostication compared to each other. The objective of this study was to investigate the performance of different MIs for survival prognostication in renal cell carcinoma (RCC). We retrospectively enrolled 214 consecutive patients with RCC. We determined three L3-MIs (psoas muscle index (PMI), psoas muscle index and erector spinae index (PMI+ESI), and whole skeletal muscle index (SMI)) from preoperative CT scans. Categorization of those with low and normal muscle mass was based on the Youden Index sex-specific MI cut-offs. We determined sensitivity, specificity, and accuracy metrics for predicting 1-year, 5-year, and overall survival (OS) using Cox regression models. Low PMI, PMI+ESI, and SMI significantly predicted decreased 1-year, 5-year, and OS in uni- and multivariate models. PMI+ESI and SMI were more accurate than PMI in males, and PMI and PMI+ESI were more accurate than SMI in females in the prediction of 1-year survival. However, there were no differences in accuracies between MIs in 5-year and OS prediction. PMI+ESI performed well overall in short-term prognostication, but there were no differences between the MIs in long-term prognostication. We recommend the use of PMI+ESI for muscle evaluation, particularly when SMI cannot be evaluated.

Reduced inhibition control ability in children with ADHD due to coexisting learning disorders: an fNIRS study.

Inhibition control, as the core component of executive function, might play a crucial role in the understanding of attention deficit/hyperactivity disorder (ADHD) and specific learning disorders (SLD). Inhibition control deficits have been observed in children with ADHD or SLD. This study sought to test in a multi-modal fashion (i.e., behavior and plus brain imaging) whether inhibition control abilities would be further deteriorated in the ADHD children due to the comorbidity of SLD. A total number of 90 children (aged 6-12 years) were recruited, including 30 ADHD, 30 ADHD+SLD (children with the comorbidity of ADHD and SLD), and 30 typically developing (TD) children. For each participant, a 44-channel functional near infrared spectroscopy (fNIRS) equipment was first adopted to capture behavioral and cortical hemodynamic responses during a two-choice Oddball task (a relatively new inhibition control paradigm). Then, 50 metrics were extracted, including 6 behavioral metrics (i.e., OddballACC, baselineACC, totalACC, OddballRT, baselineRT, and totalRT) and 44 beta values in 44 channels based on general linear model. Finally, differences in those 50 metrics among the TD, ADHD, and ADHD+SLD children were analyzed. Findings showed that: (1) OddballACC (i.e., the response accuracy in deviant stimuli) is the most sensitive metric in identifying the differences between the ADHD and ADHD+SLD children; and (2) The ADHD+SLD children exhibited decreased behavioral response accuracy and brain activation level in some channels (e.g., channel CH35) than both the ADHD and TD children. Findings seem to support that inhibition control abilities would be further decreased in the ADHD children due to the comorbidity of SLD.

The curvature quantification of waveI in auditory brainstem responses detects cochlear synaptopathy in human beings.

Patients with age-related hearing loss complain often about reduced speech perception in adverse listening environment. Studies on animals have suggested that cochlear synaptopathy may be one of the primary mechanisms responsible for this phenomenon. A decreased waveI amplitude in supra-threshold auditory brainstem response (ABR) can diagnose this pathology non-invasively. However, the interpretation of the waveI amplitude in humans remains controversial. Recent studies in mice have established a robust and reliable mathematic algorithm, i.e., curve curvature quantification, for detecting cochlear synaptopathy. This study aimed to determine whether the curve curvature has sufficient test-retest reliability to detect cochlear synaptopathy in aging humans. Healthy participants were recruited into this prospective study. All subjects underwent an audiogram examination with standard and extended high frequencies ranging from 0.125 to 16kHz and an ABR with a stimulus of 80dBnHL click. The peak amplitude, peak latency, curvature at the peak, and the area under the curve of waveI were calculated and analyzed. A total of 80 individuals with normal hearing, aged 18 to 61 years, participated in this study, with a mean age of 26.4 years.Pearson correlation analysis showed a significant negative correlation between curvature and age, as well as between curvature and extended high frequency (EHF) threshold (10-16kHz). Additionally, the same correlation was observed between age and area as well as age and EHF threshold. The model comparison demonstrated that the curvature at the peak of waveI is thebest metric to correlate with EHF threshold. The curvature at the peak of wave I is the most sensitive metric for detecting cochlear synaptopathy in humans and may be applied in routine diagnostics to detectearly degenerations of the auditory nerve.

Associations of quantitative contrast sensitivity with vascular metrics on widefield swept-source OCT angiography across stages of diabetic retinopathy

PurposeTo investigate structure–function associations between contrast sensitivity (CS) and widefield swept-source optical coherence tomography angiography (WF SS-OCTA) vascular metrics across stages of non-proliferative (NPDR) and proliferative diabetic retinopathy (PDR), without...

Apriori prediction of chemotherapy response in locally advanced breast cancer patients using CT imaging and deep learning: transformer versus transfer learning.

Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response to NAC for patients with Locally Advanced Breast Cancer (LABC) before treatment initiation could be beneficial to optimize therapy, ensuring the administration of effective treatments. The objective of the work here was to develop a predictive model to predict tumor response to NAC for LABC using deep learning networks and computed tomography (CT). Several deep learning approaches were investigated including ViT transformer and VGG16, VGG19, ResNet-50, Res-Net-101, Res-Net-152, InceptionV3 and Xception transfer learning networks. These deep learning networks were applied on CT images to assess the response to NAC. Performance was evaluated based on balanced_accuracy, accuracy, sensitivity and specificity classification metrics. A ViT transformer was applied to utilize the attention mechanism in order to increase the weight of important part image which leads to better discrimination between classes. Amongst the 117 LABC patients studied, 82 (70%) had clinical-pathological response and 35 (30%) had no response to NAC. The ViT transformer obtained the best performance range (accuracy = 71 ± 3% to accuracy = 77 ± 4%, specificity = 86 ± 6% to specificity = 76 ± 3%, sensitivity = 56 ± 4% to sensitivity = 52 ± 4%, and balanced_accuracy=69 ± 3% to balanced_accuracy=69 ± 3%) depending on the split ratio of train-data and test-data. Xception network obtained the second best results (accuracy = 72 ± 4% to accuracy = 65 ± 4, specificity = 81 ± 6% to specificity = 73 ± 3%, sensitivity = 55 ± 4% to sensitivity = 52 ± 5%, and balanced_accuracy = 66 ± 5% to balanced_accuracy = 60 ± 4%). The worst results were obtained using VGG-16 transfer learning network. Deep learning networks in conjunction with CT imaging are able to predict the tumor response to NAC for patients with LABC prior to start. A ViT transformer could obtain the best performance, which demonstrated the importance of attention mechanism.

Integration of single-cell transcriptomic and chromatin accessibility on heterogenicity of human peripheral blood mononuclear cells utilizing microwell-based single-cell partitioning technology

Abstract Single-cell RNA sequencing (scRNA-Seq) deepens our understanding of cellular development and heterogeneity. However, limitations exist in unraveling cell states and gene regulatory programs. Chromatin state profiles assess gene expression potential and offer insights into transcriptional regulation. Integrated with gene expression data, chromatin accessibility region (CAR) profiles establish fundamental gene regulatory logic for cell fate. ATAC-seq (Assay for Transposase-Accessible Chromatin using Sequencing) is a highly potent approach for profiling genome-wide CARs. To investigate the power of the multiomics assay in identifying differentiated gene regulations, we conducted multiomic snATAC-seq+ snRNA-seq on PBMCs from two different donors, by utilizing the gentle and robust microwell-based single-cell partitioning technology. The assay showed high sensitivity and specificity metrics (>10,000 median unique fragments/cell, >0.7 fragments in peak score). Integrative analysis across donors revealed enriched transcription factor motifs and fragment coverage tracks in distinct cell types, correlating significantly with gene expression data. These findings highlight mRNA's intricate connections with CARs in immune cell development. Our study underscores the power of multiomic analysis in analyzing heterogeneity of PBMC cell populations and offers a toolkit to identify gene regulation specific to diverse cell types, enhancing our comprehension of epigenetic diversity.

Machine Learning Based Classification for Spam Detection

Electronic Electronic messages, i.e. e-mails, are a communication tool frequently used by individuals or organizations. While e-mail is extremely practical to use, it is necessary to consider its vulnerabilities. Spam e-mails are unsolicited messages created to promote a product or service, often sent frequently. It is very important to classify incoming e-mails in order to protect against malware that can be transmitted via e-mail and to reduce possible unwanted consequences. Spam email classification is the process of identifying and distinguishing spam emails from legitimate emails. This classification can be done through various methods such as keyword filtering, machine learning algorithms and image recognition. The goal of spam email classification is to prevent unwanted and potentially harmful emails from reaching the user's inbox. In this study, Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM) and Artificial Neural Network (ANN) algorithms are used to classify spam emails and the results are compared. Algorithms with different approaches were used to determine the best solution for the problem. 5558 spam and non-spam e-mails were analyzed and the performance of the algorithms was reported in terms of accuracy, precision, sensitivity and F1-Score metrics. The most successful result was obtained with the RF algorithm with an accuracy of 98.83%. In this study, high success was achieved by classifying spam emails with machine learning algorithms. In addition, it has been proved by experimental studies that better results are obtained than similar studies in the literature.

Machine Learning-driven Lung Cancer Detection

Machine Learning-driven Lung Cancer Detection

P150 Diagnostic utility of anti-cN1A autoantibody testing in sporadic inclusion body myositis

Abstract Background/Aims Sporadic inclusion body myositis (sIBM) is the most common acquired myopathy in patients aged over 50 years. Diagnostic criteria encompass clinical, biochemical, and myopathological features. Certain myopathological features possess high specificity but low sensitivity; reliance on such findings may lead to delayed or misdiagnoses. The discovery of autoantibodies against cytosolic 5'-Nucleotidase 1A (cN1A) in sIBM patients suggests an autoimmune component in pathogenesis, but the inclusion of this autoantibody in diagnostic criteria remains uncertain. Additionally, optimal testing methodology has not been established, with varying techniques (e.g., ELISA/line-blot) in use. We assessed antibody profiles in an sIBM cohort and reviewed diagnoses in anti-cN1A positive patients. Methods This study was based at the Northern Care Alliance NHS Foundation Trust which hosts a tertiary neuromuscular referral service for the North-West of England. Data sources included myositis clinic and immunology results databases. All patients tested using a validated myositis line-blot immunoassay (EUROLINE 16 Antigen et cN1A [EUROIMMUN]) November 2021-April 2023 were included. Diagnoses of all tested patients were identified through case-note review. Sensitivity and specificity metrics were calculated based on diagnosis and anti-cN1A antibody status. Results The total number of patients tested for anti-cN1A was 495. Of these, 10 patients had a diagnosis of sIBM. The true-positive and false-negative rates were both 50% (n = 5 each). Of the 485 patients who did not have sIBM, true-negativity was 97.52% (n = 473) and false-positivity 2.47% (n = 12). Overall, anti-cN1A had a sensitivity of 50%, specificity of 97.52%, positive predictive value of 29.41% and negative predictive value of 98.95% for sIBM (Table 1). Patients with a false-positive anti-cN1A antibody had diagnoses of: primary Raynaud’s (n = 5); undifferentiated CTD (n = 3); systemic sclerosis (n = 2); non-sIBM myositis (n = 2). Conclusion Anti-cN1A detected using the EUROLINE 16 Antigen assay has high specificity but lacks sensitivity for sIBM, consistent with previous literature. These findings suggest that this assay performs similarly to other cN1A detection methodologies and has utility in confirming the diagnosis of sIBM where there is clinical suspicion. Further real-world analysis of antibody data in larger cohorts is required to clarify the role of serological testing in sIBM diagnostic criteria, to improve diagnostic delays and misclassifications. Disclosure M. Al-Attar: None. T. Khoo: None. J.B. Lilleker: None. H. Chinoy: None.

The Detection of Pulp Stones with Automatic Deep Learning in Panoramic Radiographies: An AI Pilot Study.

This study aims to evaluate the effectiveness of employing a deep learning approach for the automated detection of pulp stones in panoramic imaging. A comprehensive dataset comprising 2409 panoramic radiography images (7564 labels) underwent labeling using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset was stratified into three distinct subsets: training (n = 1929, 80% of the total), validation (n = 240, 10% of the total), and test (n = 240, 10% of the total) sets. To optimize the visual clarity of labeled regions, a 3 × 3 clash operation was applied to the images. The YOLOv5 architecture was employed for artificial intelligence modeling, yielding F1, sensitivity, and precision metrics of 0.7892, 0.8026, and 0.7762, respectively, during the evaluation of the test dataset. Among deep learning-based artificial intelligence algorithms applied to panoramic radiographs, the use of numerical identification for the detection of pulp stones has achieved remarkable success. It is expected that the success rates of training models will increase by using datasets consisting of a larger number of images. The use of artificial intelligence-supported clinical decision support system software has the potential to increase the efficiency and effectiveness of dentists.