Diagnostic Modalities for Early Detection of Anastomotic Leak After Colorectal Surgery.

INTRODUCTION: Anastomotic leak (AL) remains a severe complication following colorectal surgery, leading to increased morbidity and mortality, particularly in cases of delayed diagnosis. Existing diagnostic methods, including computed tomography (CT) scans, contrast enemas, endoscopic examinations, and reoperations can confirm AL but lack strong predictive value. Early detection is crucial for improving patient outcomes, yet a definitive and reliable predictive test, or "gold standard," is still lacking. METHODS: A comprehensive PubMed review was focused on CT imaging, serum levels of C-reactive protein (CRP), and procalcitonin (PCT) to assess their predictive utility in detecting AL after colorectal resection. Three independent reviewers evaluated eligibility, extracted data, and assessed the methodological quality of the studies. RESULTS: Summarized in detailed tables, our analysis revealed the effectiveness of both CRP and PCT in the early detection of AL during the postoperative period. CT imaging, capable of identifying fluid collection, pneumoperitoneum, extraluminal contrast extravasation, abscess formation, and other early signs of leak, also proved valuable. CONCLUSIONS: Considering the variability in findings and statistics across these modalities, our study suggests a personalized, multimodal approach to predicting AL. Integrating CRP and PCT assessments with the diagnostic capabilities of CT imaging provides a nuanced, patient-specific strategy that significantly enhances early detection and management. By tailoring interventions based on individual clinical characteristics, surgeons can optimize patient outcomes, reduce morbidity, and mitigate the consequences associated with AL after colorectal surgery. This approach emphasizes the importance of personalized medicine in surgical care, paving the way for improved patient health outcomes.

Segmented X-ray image data for diagnosing dental periapical diseases using deep learning.

The study presents a segmented dataset comprising dental periapical X-ray images from both healthy and diseased patients. The ability to differentiate between normal and abnormal dental periapical X-rays is pivotal for accurate diagnosis of dental pathology. These X-rays contain crucial information, offering in- sights into the physiological and pathological conditions of teeth and surrounding structures. The dataset outlined in this article encompasses dental periapical X-ray images obtained during routine examinations and treatment procedures of patients at the oral and dental health department of a local government hos- pital in North Jordan. Comprising a total of 929 high-quality X-ray images, the dataset includes subjects of varying ages with a spectrum of dental and pulpal diseases, bone loss, periapical diseases, and other abnormalities. Employing an advanced image segmentation approach, the collected dataset is categorized into healthy and diseased dental patients. This labelled dataset serves as a foundation for the development of an automated system capable of detecting dental pathologies, including caries and pulpal diseases, and distinguishing between normal and abnormal cases. Notably, recent advancements in deep learning artificial intelligence have significantly contributed to the creation of advanced dental models for diverse applications. This technology has demonstrated remarkable accuracy in the development of diagnostic and detection tools for various dental problems.

Speculative computing for AAFM solutions in large-scale product configurations.

Parallel computing is a current algorithmic approach to looking for efficient solutions; that is, to define a set of processes in charge of performing at the same time the same task. Advances in hardware permit the massification of accessibility to and applications of parallel computing. Nonetheless, some algorithms include steps that require or depend on the results of other steps that cannot be parallelized. Speculative computing allows parallelizing those tasks and reviewing different execution flows, which can involve executing invalid steps. Speculative computing solutions should reduce those invalid flows. Product configuration refers to selecting features from a set of available options respecting some configuration constraints; a not complex task for small configurations and models, but a complex one for large-scale scenarios. This article exemplifies a videogame product line feature model and a few configurations, valid and non-valid, respectively. Configuring products of large-scale feature models is a complex and time-demanding task requiring algorithmic solutions. Hence, parallel solutions are highly desired to assist the feature model product configuration tasks. Existing solutions follow a sequential computing approach and include steps that depend on others that cannot be parallelized at all, where the speculative computing approach is necessary. This article describes traditional sequential solutions for conflict detection and diagnosis, two relevant tasks in the automated analysis of feature models, and how to define their speculative parallel version, highlighting their computing improvements. Given the current parallel computing world, we remark on the advantages and current applicability of speculative computing for producing faster algorithmic solutions.

Residual Learning Diagnosis Detection: An Advanced Residual Learning Diagnosis Detection System for COVID-19 in Industrial Internet of Things.

Due to the fast transmission speed and severe health damage, COVID-19 has attracted global attention. Early diagnosis and isolation are effective and imperative strategies for epidemic prevention and control. Most diagnostic methods for the COVID-19 is based on nucleic acid testing (NAT), which is expensive and time-consuming. To build an efficient and valid alternative of NAT, this article investigates the feasibility of employing computed tomography images of lungs as the diagnostic signals. Unlike normal lungs, parts of the lungs infected with the COVID-19 developed lesions, ground-glass opacity, and bronchiectasis became apparent. Through a public dataset, in this article, we propose an advanced residual learning diagnosis detection (RLDD) scheme for the COVID-19 technique, which is designed to distinguish positive COVID-19 cases from heterogeneous lung images. Besides the advantage of high diagnosis effectiveness, the designed residual-based COVID-19 detection network can efficiently extract the lung features through small COVID-19 samples, which removes the pretraining requirement on other medical datasets. In the test set, we achieve an accuracy of 91.33%, a precision of 91.30%, and a recall of 90%. For the batch of 150 samples, the assessment time is only 4.7 s. Therefore, RLDD can be integrated into the application programming interface and embedded into the medical instrument to improve the detection efficiency of COVID-19.

Detection and segmentation of multi-class artifacts in endoscopy.

Endoscopy may be used for early screening of various cancers, such as nasopharyngeal cancer, esophageal adenocarcinoma, gastric cancer, colorectal cancer, and bladder cancer, and performing minimal invasive surgical procedures, such as laparoscopy surgery. During this procedure, an endoscope is used; it is a long, thin, rigid, or flexible tube having a light source and a camera at the tip, which facilitates visualization inside the affected organs on a screen and helps doctors in diagnosis.

[Researching on the Classification of Computer Aided Diagnosis/Detection Software].

Based on the developing situation of Computer Aided Diagnosis/Detection (CAD) software, considering the domestic and international regulation of CAD software, according to current Medical Device Classification Catalog and related laws of China Food and Drug Administration (CFDA), this paper investigated and analyzed the classification of CAD software, and provided technical suggestion on classifying principle of CAD software applying Artificial Intelligence (AI) or other advanced technology from medical device regulation scope, for the reference of regulatory and technical departments.

Researching on the Classification of Computer Aided Diagnosis/Detection Software / 中国医疗器械杂志

Based on the developing situation of Computer Aided Diagnosis/Detection (CAD) software, considering the domestic and international regulation of CAD software, according to current Medical Device Classification Catalog and related laws of China Food and Drug Administration (CFDA), this paper investigated and analyzed the classification of CAD software, and provided technical suggestion on classifying principle of CAD software applying Artificial Intelligence (AI) or other advanced technology from medical device regulation scope, for the reference of regulatory and technical departments.

Artificial Intelligence: Threat or Boon to Radiologists?

The development and integration of machine learning/artificial intelligence into routine clinical practice will significantly alter the current practice of radiology. Changes in reimbursement and practice patterns will also continue to affect radiology. But rather than being a significant threat to radiologists, we believe these changes, particularly machine learning/artificial intelligence, will be a boon to radiologists by increasing their value, efficiency, accuracy, and personal satisfaction.

Systematic review of methods to predict and detect anastomotic leakage in colorectal surgery.

AIM: Anastomotic leakage is a serious complication of gastrointestinal surgery resulting in increased morbidity and mortality, poor function and predisposing to cancer recurrence. Earlier diagnosis and intervention can minimize systemic complications but is hindered by current diagnostic methods that are non-specific and often uninformative. The purpose of this paper is to review current developments in the field and to identify strategies for early detection and treatment of anastomotic leakage. METHOD: A systematic literature search was performed using the MEDLINE, Embase, PubMed and Cochrane Library databases. Search terms included 'anastomosis' and 'leak' and 'diagnosis' or 'detection' and 'gastrointestinal' or 'colorectal'. Papers concentrating on the diagnosis of gastrointestinal anastomotic leak were identified and further searches were performed by cross-referencing. RESULTS: Computerized tomography CT scanning and water-soluble contrast studies are the current preferred techniques for diagnosing anastomotic leakage but suffer from variable sensitivity and specificity, have logistical constraints and may delay timely intervention. Intra-operative endoscopy and imaging may offer certain advantages, but the ability to predict anastomotic leakage is unproven. Newer techniques involve measurement of biomarkers for anastomotic leakage and have the potential advantage of providing cheap real-time monitoring for postoperative complications. CONCLUSION: Current diagnostic tests often fail to diagnose anastomotic leak at an early stage that enables timely intervention and minimizes serious morbidity and mortality. Emerging technologies, based on detection of local biomarkers, have achieved proof of concept status but require further evaluation to determine whether they translate into improved patient outcomes. Further research is needed to address this important, yet relatively unrecognized, area of unmet clinical need.

Impact of lesion segmentation metrics on computer-aided diagnosis/detection in breast computed tomography.

Evaluation of segmentation algorithms usually involves comparisons of segmentations to gold-standard delineations without regard to the ultimate medical decision-making task. We compare two segmentation evaluations methods-a Dice similarity coefficient (DSC) evaluation and a diagnostic classification task-based evaluation method using lesions from breast computed tomography. In our investigation, we use results from two previously developed lesion-segmentation algorithms [a global active contour model (GAC) and a global with local aspects active contour model]. Although similar DSC values were obtained (0.80 versus 0.77), we show that the global + local active contour (GLAC) model, as compared with the GAC model, is able to yield significantly improved classification performance in terms of area under the receivers operating characteristic (ROC) curve in the task of distinguishing malignant from benign lesions. [Area under the [Formula: see text] compared to 0.63, [Formula: see text]]. This is mainly because the GLAC model yields better detailed information required in the calculation of morphological features. Based on our findings, we conclude that the DSC metric alone is not sufficient for evaluating segmentation lesions in computer-aided diagnosis tasks.