Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?

INTRODUCTION: We aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopathies can be diagnosed with these deep learning methods without radiologist interpretations and histopathological examinations. MATERIAL METHOD: A total of 400 patients who underwent surgery for lymphadenopathy in the neck between 2010 and 2022 were retrospectively analyzed. They were examined in four groups of 100 patients: the granulomatous diseases group, the lymphoma group, the squamous cell tumor group, and the reactive hyperplasia group. The diagnoses of the patients were confirmed histopathologically. Two CT images from all the patients in each group were used in the study. The CT images were classified using ResNet50, NASNetMobile, and DenseNet121 architecture input. RESULTS: The classification accuracies obtained with ResNet50, DenseNet121, and NASNetMobile were 92.5%, 90.62, and 87.5, respectively. CONCLUSION: Deep learning is a useful diagnostic tool in diagnosing cervical lymphadenopathy. In the near future, many diseases could be diagnosed with deep learning models without radiologist interpretations and invasive examinations such as histopathological examinations. However, further studies with much larger case series are needed to develop accurate deep-learning models.

Automatic detection of brain tumors with the aid of ensemble deep learning architectures and class activation map indicators by employing magnetic resonance images.

Today, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal cells begin to form in masses in the brain regions. Nowadays, many different techniques are employed to detect these tumor masses, and the most common of these techniques is Magnetic Resonance Imaging (MRI). In this study, it is aimed to automatically detect brain tumors with the help of ensemble deep learning architectures (ResNet50, VGG19, InceptionV3 and MobileNet) and Class Activation Maps (CAMs) indicators by employing MRI images. The proposed system was implemented in three stages. In the first stage, it was determined whether there was a tumor in the MR images (Binary Approach). In the second stage, different tumor types (Normal, Glioma Tumor, Meningioma Tumor, Pituitary Tumor) were detected from MR images (Multi-class Approach). In the last stage, CAMs of each tumor group were created as an alternative tool to facilitate the work of specialists in tumor detection. The results showed that the overall accuracy of the binary approach was calculated as 100% on the ResNet50, InceptionV3 and MobileNet architectures, and 99.71% on the VGG19 architecture. Moreover, the accuracy values of 96.45% with ResNet50, 93.40% with VGG19, 85.03% with InceptionV3 and 89.34% with MobileNet architectures were obtained in the multi-class approach.

Effects of showing the operating room on preoperative anxiety and hemodynamics among patients with hypertension: A randomized controlled trial.

PURPOSE: Anxiety is one of the most important causes of hypertension, increasing direct blood pressure and affecting postoperative morbidity and mortality. The aim of this study was to investigate the effects of showing the operating room on preoperative anxiety and hemodynamics among patients with hypertension. METHODS: We enrolled 90 patients with hypertension undergoing cholecystectomy in this trial. Patients were randomly divided into two groups using a sealed-envelope system. Group STOR was shown the operating room the day before surgery, while Group No STOR was not shown the operating room. RESULTS: State-Trait Anxiety Inventory scores measured on the day of surgery were lower for Group STOR (43.2 ± 6.0) than Group No STOR (49.8 ± 7.9) (p = .001). Systolic (p = .001, p = .006, respectively), diastolic (p = .001, p = .004, respectively), and heart rate (p = .018, p = .031, respectively) values in the operation room and preoperative unit were lower in Group STOR than in Group No STOR. The number of postponed operations in Group STOR was lower than in Group No STOR (p = .043), and the patient satisfaction score in Group STOR was higher than in Group No STOR (p = .031). CONCLUSION: In patients with hypertension, preoperative anxiety, blood pressure, heart rate, and respiratory rate all increase in the preoperative unit and operation room. Our findings indicate that showing the operating room to patients with hypertension decreases preoperative anxiety, as well as blood pressure and heart rate inside the operating room and preoperative unit. It also reduces the number of postponed operations and increases patient satisfaction.