Purpose: This study aims to automatically classify color Doppler images into two categories for stroke risk prediction based on the carotid plaque. The first category is high-risk carotid vulnerable plaque, and the second is stable carotid plaque.Method: In this research study, we used a deep learning framework based on transfer learning to classify color Doppler images into two categories: one is high-risk carotid vulnerable plaque, and the other is stable carotid plaque. The data were collected from the Second Affiliated Hospital of Fujian Medical University, including stable and vulnerable cases. A total of 87 patients with risk factors for atherosclerosis in our hospital were selected. We used 230 color Doppler ultrasound images for each category and further divided those into the training set and test set in a ratio of 70 and 30%, respectively. We have implemented Inception V3 and VGG-16 pre-trained models for this classification task.Results: Using the proposed framework, we implemented two transfer deep learning models: Inception V3 and VGG-16. We achieved the highest accuracy of 93.81% by using fine-tuned and adjusted hyperparameters according to our classification problem.Conclusion: In this research, we classified color Doppler ultrasound images into high-risk carotid vulnerable and stable carotid plaques. We fine-tuned pre-trained deep learning models to classify color Doppler ultrasound images according to our dataset. Our suggested framework helps prevent incorrect diagnoses caused by low image quality and individual experience, among other factors.

This meta-analysis evaluated the diagnostic value of ultrasonic elastography in detecting placental stiffness in the diagnosis of preeclampsia (PE). A systematic search was conducted in the EMBASE, Web of Science, Cochrane Library, Scopus database, and PubMed databases to identify studies published before June 2023 using ultrasonic elastography to diagnose PE. The sensitivity, specificity, and diagnostic odds ratio of ultrasonic elastography for diagnosing PE were calculated, and a summary receiver operating characteristic curve model was constructed. The degree of heterogeneity was estimated using the I² statistic, and a meta-regression analysis was performed to explore its sources. A protocol was determined previously (PROSPERO: CRD42023443646). We included 1188 participants from 11 studies, including 190 patients with PE and 998 patients without PE as controls. Overall sensitivity and specificity of ultrasonic elastography in detecting placental stiffness for the diagnosis of PE were 89% (95% CI: 85–93) and 74% (95% CI: 51–89), respectively. The I² values for sensitivity and specificity were 59% (95% CI: 29–89) and 96% (95% CI: 95–98), respectively. The area under the receiver operating characteristic curve was 0.90 (95% CI: 0.87–0.92). The meta-regression analysis showed no significant heterogeneity. Ultrasonic elastography exhibits good diagnostic accuracy for detecting placental stiffness and can serve as a non-invasive tool for differentially diagnosing PE.

Background: We aimed to develop a nomogram that can be combined with point-of-care gastric ultrasound and utilised to predict postoperative nausea and vomiting (PONV) in adult patients after emergency surgery. Methods: Imaging and clinical data of 236 adult patients undergoing emergency surgery in a university hospital between April 2022 and February 2023 were prospectively collected. Patients were divided into a training cohort (n = 177) and a verification cohort (n = 59) in a ratio of 3:1, according to a random number table. After univariate analysis and multivariate logistic regression analysis of the training cohort, independent risk factors for PONV were screened to develop the nomogram model. The receiver operating characteristic curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) were used to evaluate the prediction efficiency, accuracy, and clinical practicability of the model. Results: Univariate analysis and multivariate logistic regression analysis showed that female sex, history of PONV, history of migraine and gastric cross-sectional area were independent risk factors for PONV. These four independent risk factors were utilised to construct the nomogram model, which achieved significant concordance indices of 0.832 (95% confidence interval [CI], 0.771–0.893) and 0.827 (95% CI, 0.722–0.932) for predicting PONV in the training and validation cohorts, respectively. The nomogram also had well-fitted calibration curves. DCA and CIC indicated that the nomogram had great clinical practicability. Conclusions: This study demonstrated the prediction efficacy, differentiation, and clinical practicability of a nomogram for predicting PONV. This nomogram may serve as an intuitive and visual guide for rapid risk assessment in patients with PONV before emergency surgery.

Objective: Systemic lupus erythematosus (SLE) is a systemic autoimmune disease with the main clinical characteristics of multisystem and multiorgan involvement of the whole body. It is commonly seen in skin mucosa, skeletal muscle, and the respiratory system while rarely involving the spleen. In this case, we present a young female patient with SLE with the symptom of progressing splenic calcification.Methods: A 21–year–old female patient was admitted to our hospital in 2012 with complaints of “fever, abdominal pain, diarrhea, and knee pain”, and then was diagnosed with systemic lupus erythematosus combined with lupus nephritis. The first ultrasound examination was normal in 2012. However, when she returned to the hospital in 2016, she was found to have multiple calcifications in her spleen. After discharge, the patient was treated with commonly used immunosuppressive drugs and was followed up regularly for symptoms of multiple calcifications in the spleen.Results: Subsequent follow–up over a long period revealed a progressive development of multiple calcifications in the spleen, with a progressive increase in the size and number of calcified foci over time.Conclusion: When a patient is found to have diffuse splenic calcification on ultrasound, care should be taken to differentiate SLE with multiple splenic calcifications from sarcoidosis, tuberculosis, brucellosis, and rheumatoid arthritis in conjunction with a medical history and relevant laboratory tests.

Background: This study evaluates the performance of GPT-4o in detecting errors in ACR TIRADS ultrasound reports and its potential to reduce report generation time. Methods: A retrospective analysis of 200 thyroid ultrasound reports from the Second Affiliated Hospital of Fujian Medical University was conducted, with reports categorized as correct or containing up to three errors. GPT-4o's performance was compared with ultrasound physicians of varying experience levels in error detection and processing time. Results: GPT-4o detected 90.0% (180/200) of errors, slightly less than the best-performing senior ultrasound physician's 93.0% (186/200) with no significant difference ( p =  0.281). GPT-4o's error detection rate was comparable to that of ultrasound physicians overall ( p =  0.098 to 0.866). It outperformed Resident 2 in diagnostic errors (87% vs. 69%). Reader agreement was low (Cohen's kappa = 0 to 0.31). GPT-4o reviewed reports significantly faster than all ultrasound physicians (0.79 vs. 1.8 to 3.1 h, p  < 0.001), making it a reliable and efficient tool for error detection in medical imaging. Conclusions: GPT-4o is comparable to experienced ultrasound physicians in error detection and significantly improves report processing efficiency, offering a valuable tool for enhancing diagnostic accuracy and aiding junior residents.