Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images

Lung diseases such as COVID-19, tuberculosis (TB), and pneumonia continue to be serious global health concerns that affect millions of people worldwide. In medical practice, chest X-ray examinations have emerged as the norm for diagnosing diseases, particularly chest infections such as COVID-19. Paramedics and scientists are working intensively to create a reliable and precise approach for early-stage COVID-19 diagnosis in order to save lives. But with a variety of symptoms, medical diagnosis of these disorders poses special difficulties. It is essential to address their identification and timely diagnosis in order to successfully treat and prevent these illnesses. In this research, a multiclass classification approach using state-of-the-art methods for deep learning and image processing is proposed. This method takes into account the robustness and efficiency of the system in order to increase diagnostic precision of chest diseases. A comparison between a brand-new convolution neural network (CNN) and several transfer learning pre-trained models including VGG19, ResNet, DenseNet, EfficientNet, and InceptionNet is recommended. Publicly available and widely used research datasets like Shenzen, Montogomery, the multiclass Kaggle dataset and the NIH dataset were used to rigorously test the model. Recall, precision, F1-score, and Area Under Curve (AUC) score are used to evaluate and compare the performance of the proposed model. An AUC value of 0.95 for COVID-19, 0.99 for TB, and 0.98 for pneumonia is obtained using the proposed network. Recall and precision ratings of 0.95, 0.98, and 0.97, respectively, likewise met high standards.Comment: 11 pages, 18 figures, Under review in Discover Artificial Intelligence Journal by Springer Natur

Interrater Reliability of Functional Lumen Imaging Probe Panometry and High-Resolution Manometry for the Assessment of Esophageal Motility Disorders.

INTRODUCTION: High-resolution manometry (HRM) and functional lumen imaging probe (FLIP) are primary and/or complementary diagnostic tools for the evaluation of esophageal motility. We aimed to assess the interrater agreement and accuracy of HRM and FLIP interpretations. METHODS: Esophageal motility specialists from multiple institutions completed the interpretation of 40 consecutive HRM and 40 FLIP studies. Interrater agreement was assessed using intraclass correlation coefficient (ICC) for continuous variables and Fleiss κ statistics for nominal variables. Accuracies of rater interpretation were assessed using the consensus of 3 experienced raters as the reference standard. RESULTS: Fifteen raters completed the HRM and FLIP studies. An excellent interrater agreement was seen in supine median integral relaxation pressure (ICC 0.96, 95% confidence interval 0.95-0.98), and a good agreement was seen with the assessment of esophagogastric junction (EGJ) outflow, peristalsis, and assignment of a Chicago Classification version 4.0 diagnosis using HRM (κ = 0.71, 0.75, and 0.70, respectively). An excellent interrater agreement for EGJ distensibility index and maximum diameter (0.91 [0.90-0.94], 0.92 [0.89-0.95]) was seen, and a moderate-to-good agreement was seen in the assignment of EGJ opening classification, contractile response pattern, and motility classification (κ = 0.68, 0.56, and 0.59, respectively) on FLIP. Rater accuracy for Chicago Classification version 4.0 diagnosis on HRM was 82% (95% confidence interval 78%-84%) and for motility diagnosis on FLIP Panometry was 78% (95% confidence interval 72%-81%). DISCUSSION: Our study demonstrates high levels of interrater agreement and accuracy in the interpretation of HRM and FLIP metrics and moderate-to-high levels for motility classification in FLIP, supporting the use of these approaches for primary or complementary evaluation of esophageal motility disorders

Classifying Esophageal Motility by FLIP Panometry: A Study of 722 Subjects With Manometry.

INTRODUCTION: Functional luminal imaging probe (FLIP) panometry can evaluate esophageal motility in response to sustained esophageal distension at the time of sedated endoscopy. This study aimed to describe a classification of esophageal motility using FLIP panometry and evaluate it against high-resolution manometry (HRM) and Chicago Classification v4.0 (CCv4.0). METHODS: Five hundred thirty-nine adult patients who completed FLIP and HRM with a conclusive CCv4.0 diagnosis were included in the primary analysis. Thirty-five asymptomatic volunteers (controls) and 148 patients with an inconclusive CCv4.0 diagnosis or systemic sclerosis were also described. Esophagogastric junction (EGJ) opening and the contractile response (CR) to distension (i.e., secondary peristalsis) were evaluated with a 16-cm FLIP during sedated endoscopy and analyzed using a customized software program. HRM was classified according to CCv4.0. RESULTS: In the primary analysis, 156 patients (29%) had normal motility on FLIP panometry, defined by normal EGJ opening and a normal or borderline CR; 95% of these patients had normal motility or ineffective esophageal motility on HRM. Two hundred two patients (37%) had obstruction with weak CR, defined as reduced EGJ opening and absent CR or impaired/disordered CR, on FLIP panometry; 92% of these patients had a disorder of EGJ outflow per CCv4.0. DISCUSSION: Classifying esophageal motility in response to sustained distension with FLIP panometry parallels the swallow-associated motility evaluation provided with HRM and CCv4.0. Thus, FLIP panometry serves as a well-tolerated method that can complement, or in some cases be an alternative to HRM, for evaluating esophageal motility disorders