Diagnostic models for predicting malignancy in thyroid nodules classified as Bethesda Category III in an endemic region.

Background: This study aims to develop a diagnostic model to help physicians determine whether thyroid nodules categorized as atypia of undetermined significance (AUS) in category III of the Bethesda system are benign or malignant preoperatively. To create a diagnostic model for predicting thyroid nodules' benign or malignant with AUS cytology based on clinical, ultrasonographic, and cytopathological findings.

Methods: This is a retrospective cohort study involving patients (>19) at risk of thyroid cancer who had thyroidectomy after an AUS cytology.

A novel computer based risk prediction model for vocal cord palsy before thyroidectomy.

Background: Thyroidectomy may be performed for clinical indications that include malignancy, benign nodules or cysts suspicious findings on fine needle aspiration (FNA) biopsy, dyspnea from airway compression or dysphagia from cervical esophageal compression, etc. The incidences of vocal cord palsy (VCP) caused by thyroid surgery were reported to range from 3.4% to 7.

Prediction of gestational diabetes using deep learning and Bayesian optimization and traditional machine learning techniques.

The study aimed to develop a clinical diagnosis system to identify patients in the GD risk group and reduce unnecessary oral glucose tolerance test (OGTT) applications for pregnant women who are not in the GD risk group using deep learning algorithms. With this aim, a prospective study was designed and the data was taken from 489 patients between the years 2019 and 2021, and informed consent was obtained. The clinical decision support system for the diagnosis of GD was developed using the generated dataset with deep learning algorithms and Bayesian optimization.

A prospective study for gestational diabetes mellitus: Analysis of risk factors in Turkish women for early prediction.

Objective: To define risk factors for the early prediction of gestational diabetes mellitus (GDM) because the risk of pre-eclampsia and preterm birth increases in mothers who are diagnosed with GDM.

Materials And Methods: A prospective study was designed and the data were collected by physicians prospectively from the patients who came to the clinic between the years 2019 and 2021; informed consent was obtained from the women. The prospective data comprised 489 patient records with 72 variables and the risk factors for early prediction of GDM were determined using logistic regression and random forest (RF), which is an advanced analysis method.

Comparison of enhancement methods for mammograms with performance measures.

Mammograms are generally contaminated by noise which assures the need for image enhancement to aid interpretation. The enhancement of mammograms is a very important problem for easy extraction of suspicious regions known as regions of interest (ROIs). This paper introduces comparison of various hybrid enhancement algorithms based on mathematical morphology, contrast stretching, wavelet transform, anisotropic diffusion filter and contrast limited adaptive histogram equalization (CLAHE).

A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding.

Mass detection is a very important process for breast cancer diagnosis and computer aided systems. It can be very complex when the mass is small or invisible because of dense breast tissue. Therefore, the extraction of suspicious mass region can be very challenging.