Comparative Efficacy of Micro-Needle-Knife Therapy and Acupuncture in Acute Ankle Sprains: A Randomized Controlled Trial.

BACKGROUND Micro-needle knife (MNK) therapy releases the superficial fascia to alleviate pain and improve joint function in patients with acute ankle sprains (AAS). We aimed to evaluate the efficacy and safety of MNK therapy vs that of acupuncture. MATERIAL AND METHODS This blinded assessor, randomized controlled trial allocated 80 patients with AAS to 2 parallel groups in a 1: 1 ratio.

Effect of glycotoxicity and lipotoxicity on carbohydrate antigen 19 - 9 in the patients with diabetes.

Objectives: In comparison to the subjects without diabetes, a greater concentration of serum carbohydrate antigen 19 - 9 (CA 19 - 9) was observed in the subjects with diabetes. Nevertheless, since the occurrence of abnormal CA 19 - 9 is not widespread among the whole diabetic population, this phenomenon has not attracted enough attention. The prevalence of abnormal CA 19 - 9 in hospitalized patients with diabetes was the focus of our research.

Deep learning approaches for differentiating thyroid nodules with calcification: a two-center study.

Background: Calcification is a common phenomenon in both benign and malignant thyroid nodules. However, the clinical significance of calcification remains unclear. Therefore, we explored a more objective method for distinguishing between benign and malignant thyroid calcified nodules.

AI diagnosis of Bethesda category IV thyroid nodules.

Thyroid nodules are a common disease, and fine needle aspiration cytology (FNAC) is the primary method to assess their malignancy. For the diagnosis of follicular thyroid nodules, however, FNAC has limitations. FNAC can classify them only as Bethesda IV nodules, leaving their exact malignant status and pathological type undetermined.

The auxiliary diagnosis of thyroid echogenic foci based on a deep learning segmentation model: A two-center study.

Objective: The aim of this study is to develop AI-assisted software incorporating a deep learning (DL) model based on static ultrasound images. The software aims to aid physicians in distinguishing between malignant and benign thyroid nodules with echogenic foci and to investigate how the AI-assisted DL model can enhance radiologists' diagnostic performance.

Methods: For this retrospective study, a total of 2724 ultrasound (US) scans were collected from two independent institutions, encompassing 1038 echogenic foci nodules.

Comparison of machine learning models based on multi-parametric magnetic resonance imaging and ultrasound videos for the prediction of prostate cancer.

Objective: To establish machine learning (ML) prediction models for prostate cancer (PCa) using transrectal ultrasound videos and multi-parametric magnetic resonance imaging (mpMRI) and compare their diagnostic performance.

Materials And Methods: We systematically collated the data of 383 patients, including 187 with PCa and 196 with benign lesions. Of them, 307 patients (150 with PCa and 157 with benign lesions) were randomly selected to train and validate the ML models, 76 patients were used as test set.

Value of machine learning-based transrectal multimodal ultrasound combined with PSA-related indicators in the diagnosis of clinically significant prostate cancer.

Objective: To investigate the effect of transrectal multimodal ultrasound combined with serum prostate-specific antigen (PSA)-related indicators and machine learning for the diagnosis of clinically significant prostate cancer.

Methods: Based on Gleason score of postoperative pathological results, the subjects were divided into clinically significant prostate cancer groups(GS>6)and non-clinically significant prostate cancer groups(GS ≤ 6). The independent risk factors were obtained by univariate logistic analysis.

Machine learning prediction of prostate cancer from transrectal ultrasound video clips.

Objective: To build a machine learning (ML) prediction model for prostate cancer (PCa) from transrectal ultrasound video clips of the whole prostate gland, diagnostic performance was compared with magnetic resonance imaging (MRI).

Methods: We systematically collated data from 501 patients-276 with prostate cancer and 225 with benign lesions. From a final selection of 231 patients (118 with prostate cancer and 113 with benign lesions), we randomly chose 170 for the purpose of training and validating a machine learning model, while using the remaining 61 to test a derived model.