In this study, the hypothesis that computer aided diagnosis could enable a more accurate differentiation between patients with acute appendicitis and those with abdominal pain but normal appendixes was examined. A data base was established by analyzing the records of 476 patients having an emergency measure appendectomy during a five year period. There were 360 or 76 per cent with acute appendicitis, 98 or 20 per cent with normal appendixes and 18 or 4 per cent with other diseases requiring operation. The records were analyzed with regard to history, physical examination and laboratory findings. The data base was then divided randomly into two parts. Part 1 was subjected to univariate discriminant analysis, using the chi-square test. The only quantities which were significantly different between appendicitis and a normal appendix were sex, duration of symptoms, anorexia and vomiting. Multivariate discriminant analysis was used to derive an abdominal pain index which discriminated between appendicitis and a normal appendix with a sensitivity of 0.82 and a specificity of 0.39. Using the abdominal pain index to evaluate the patients in part 2 of the data base, 23 or 40 per cent of the 58 patients with a normal appendix would have avoided operation. However, 31 or 18 per cent of the 169 patients with appendicitis would have not been operated upon; three of those 31 had perforated appendixes. Computer aided diagnosis was no more effective than unaided clinical diagnosis in appendicitis.
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BMC Cancer
January 2025
Department of Pathology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, Zhejiang, China.
Objective: Rapid on-site evaluation (ROSE) of respiratory cytology specimens is a critical technique for accurate and timely diagnosis of lung cancer. However, in China, limited familiarity with the Diff-Quik staining method and a shortage of trained cytopathologists hamper utilization of ROSE. Therefore, developing an improved deep learning model to assist clinicians in promptly and accurately evaluating Diff-Quik stained cytology samples during ROSE has important clinical value.
View Article and Find Full Text PDFSci Rep
January 2025
College of Medical Engineering and Technology, Xinjiang Medical University, Urumqi, 830017, China.
Hepatic cystic echinococcosis (HCE), a life-threatening liver disease, has 5 subtypes, i.e., single-cystic, polycystic, internal capsule collapse, solid mass, and calcified subtypes.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Gastroenterology Department of Gandhi Medical College, Bhopal, 462003, India.
Gastrointestinal tract-related cancers pose a significant health burden, with high mortality rates. In order to detect the anomalies of the gastrointestinal tract that may progress to cancer, a video capsule endoscopy procedure is employed. The number of video capsule endoscopic ( ) images produced per examination is enormous, which necessitates hours of analysis by clinicians.
View Article and Find Full Text PDFJ Prosthet Dent
January 2025
Assistant Professor, Department of Prosthodontic, College of Dental Medicine, Rangsit University, Phatum Thani, Thailand. Electronic address:
Statement Of Problem: Comprehensive data are needed on the performance of chemically activated, chairside hard reline materials when used with computer-aided design and computer-aided manufacturing (CAD-CAM) milled polymethyl methacrylate (PMMA) denture bases and conventionally processed bases. This lack of data affects decisions regarding the chairside reline material to be used for improving the fit and retention of relined complete dentures.
Purpose: The purpose of this in vitro study was to evaluate and compare the shear bond strength (SBS) of 3 chemically activated, chairside hard reline materials on CAD-CAM milled and conventional heat-polymerized PMMA denture bases.
PLoS One
January 2025
Department of Computer Science, National Textile University, Faisalabad, Pakistan.
Accurate diagnosis of pancreatic cancer using CT scan images is critical for early detection and treatment, potentially saving numerous lives globally. Manual identification of pancreatic tumors by radiologists is challenging and time-consuming due to the complex nature of CT scan images and variations in tumor shape, size, and location of the pancreatic tumor also make it challenging to detect and classify different types of tumors. Thus, to address this challenge we proposed a four-stage framework of computer-aided diagnosis systems.
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