RESUMO
Background: Thyroid nodules are commonly identified through ultrasound imaging, which plays a crucial role in the early detection of malignancy. The diagnostic accuracy, however, is significantly influenced by the expertise of radiologists, the quality of equipment, and image acquisition techniques. This variability underscores the critical need for computational tools that support diagnosis. Methods: This retrospective study evaluates an artificial intelligence (AI)-driven system for thyroid nodule assessment, integrating clinical practices from multiple prominent Thai medical centers. We included patients who underwent thyroid ultrasonography complemented by ultrasound-guided fine needle aspiration (FNA) between January 2015 and March 2021. Participants formed a consecutive series, enhancing the study's validity. A comparative analysis was conducted between the AI model's diagnostic performance and that of both an experienced radiologist and a third-year radiology resident, using a dataset of 600 ultrasound images from three distinguished Thai medical institutions, each verified with cytological findings. Results: The AI system demonstrated superior diagnostic performance, with an overall sensitivity of 80% [95% confidence interval (CI): 59.3-93.2%] and specificity of 71.4% (95% CI: 53.7-85.4%). At Siriraj Hospital, the AI achieved a sensitivity of 90.0% (95% CI: 55.5-99.8%), specificity of 100.0% (95% CI: 69.2-100%), positive prediction value (PPV) of 100.0%, negative prediction value (NPV) of 90.9%, and an overall accuracy of 95.0%, indicating the benefits of AI's extensive training across diverse datasets. The experienced radiologist's sensitivity was 40.0% (95% CI: 21.1-61.3%), while the specificity was 80.0% (95% CIs: 63.6-91.6%), respectively, showing that the AI significantly outperformed the radiologist in terms of sensitivity (P=0.043) while maintaining comparable specificity. The inter-observer variability analysis indicated a moderate agreement (K=0.53) between the radiologist and the resident, contrasting with fair agreement (K=0.37/0.33) when each was compared with the AI system. Notably, 95% CIs for these diagnostic indexes highlight the AI system's consistent performance across different settings. Conclusions: The findings advocate for the integration of AI into clinical settings to enhance the diagnostic accuracy of radiologists in assessing thyroid nodules. The AI system, designed as a supportive tool rather than a replacement, promises to revolutionize thyroid nodule diagnosis and management by providing a high level of diagnostic precision.
RESUMO
OBJECTIVE: The purpose of this study is to compare the early radiologic diagnosis of pulmonary infection between serial chest radiography (chest film) and single chest computed tomography (CT chest) in the first seven days of febrile neutropenia. METHODS: This study included 78 patients with hematologic malignancies who developed 107 episodes of febrile neutropenia from January 2012 to October 2017 and had a chest film performed within the first seven days. Demographic and radiographic data were retrospectively reviewed. Three radiologists independently and blindly evaluated chest films and CT chests. The sensitivity, specificity, and correlation of chest film with absolute neutrophil count were carried out. RESULTS: A total of 222 chest films were performed during this period and found thirty-nine episodes (36.4%) of radiographic active pulmonary infection. The diagnosis of clinical positive for pulmonary infection is 44.8% (48/107). Sensitivity, specificity, positive predictive value, and negative predictive value of serial chest film in the early radiologic diagnosis of pulmonary infection are 50%, 74%, 61%, and 64%, respectively. The false-positive rate was 14%, and the false-negative rate was 22%. For single CT chest examinations, twenty-six studies were assessed, and 42.3% was indicative of radiographic active pulmonary infection. Sensitivity, specificity, positive predictive value, and negative predictive value of CT chest in the early radiologic diagnosis of pulmonary infection are 91%, 40%, 53%, and 86%, respectively. The false-positive rate was 60%. The absolute neutrophil count was not useful for predicting radiographic active pulmonary infection. CONCLUSION: Serial chest film for early radiologic diagnosis of pulmonary infection within the first seven days of febrile neutropenia has lower sensitivity with higher specificity as compared to a single CT chest. Conversely, CT chest may not only have a higher sensitivity in determining early pulmonary infection but also has a higher rate of false-positives.
RESUMO
Trochlear pain is frequently overlooked as published data regarding the clinical characteristic and current treatment are limited. The aim of this study is to evaluate this information from our experiences with trochlear pain. Medical records of 43 patients with trochlear pain from HRH Princess Maha Chakri Sirindhorn Medical Center between November 2010 and April 2017 were reviewed. Most patients were female (88%), with a median age of 51 years. Common characteristic symptoms of trochlear pain were acute, episodic, dull or pressure-like, periorbital pain, often radiating to the forehead, and aggravated by eye movements, especially reading. The causes of trochlear pain were idiopathic or primary trochlear headache (n = 33, 77%) and trochleitis (n = 10, 23%). Treatments included oral NSAIDs or dexamethasone injection into the trochlear region. At a median follow-up of 11 months (range 0-64), 67% of the patients reported complete remission using oral medication. Local steroid injection is useful in non-responding patients to oral therapy with an overall remission of 86%. Successful treatment outcome was achieved in most patients.
