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BACKGROUND: At present, most articles mainly focused on the diagnosis of thyroid nodules by using artificial intelligence (AI), and there was little research on the detection performance of AI in thyroid nodules. OBJECTIVE: To explore the value of a real-time AI based on computer-aided diagnosis system in the detection of thyroid nodules and to analyze the factors influencing the detection accuracy. METHODS: From June 1, 2022 to December 31, 2023, 224 consecutive patients with 587 thyroid nodules were prospective collected. Based on the detection results determined by two experienced radiologists (both with more than 15 years experience in thyroid diagnosis), the detection ability of thyroid nodules of radiologists with different experience levels (junior radiologist with 1 year experience and senior radiologist with 5 years experience in thyroid diagnosis) and real-time AI were compared. According to the logistic regression analysis, the factors influencing the real-time AI detection of thyroid nodules were analyzed. RESULTS: The detection rate of thyroid nodules by real-time AI was significantly higher than that of junior radiologist (P = 0.013), but lower than that of senior radiologist (P = 0.001). Multivariate logistic regression analysis showed that nodules size, superior pole, outside (near carotid artery), close to vessel, echogenicity (isoechoic, hyperechoic, mixed-echoic), morphology (not very regular, irregular), margin (unclear), ACR TI-RADS category 4 and 5 were significant independent influencing factors (all P < 0.05). With the combination of real-time AI and radiologists, junior and senior radiologist increased the detection rate to 97.4% (P < 0.001) and 99.1% (P = 0.015) respectively. CONCLUSONS: The real-time AI has good performance in thyroid nodule detection and can be a good auxiliary tool in the clinical work of radiologists. [ABSTRACT FROM AUTHOR]

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A reversed-phase high performance liquid chromatographic method was developed for the determination of o-cymene-5-ol in oral care and cleansing products such as toothpastes and mouthwash. Samples were extracted by 50% methanol aqueous solution, and preprocessed by magnetic stirring, ultrasonic extraction, centrifugation and filtration. Dionex Acclaim™M 120 C18 was used as a chromatographic column and acetonitrile-10 mmol/L monopotassium phosphate as mobile phase for gradient elution. The flow rate was 1.5 mL/min, and the column temperature was 30 °C. The detection was carried out by a diode array detector with the wavelength of 279 nm. The external standard method was used for quantitative analysis. The mass concentration of o-cymene-5-ol in the range of 1.00-15.0 mg/L shows a good linear relationship with the peak area. The correlation coefficient is 0.999 9, the detection limit is 20 mg/kg and the quantitative limit is 60 mg/kg. The recovery rate is 96.3%-101.4%, and the relative standard deviation is 0.41%-1.63% (n=6). The method is simple, sensitive and reproducible. It is suitable for the determination of o-cymene-5-ol in oral care and cleansing products. [ABSTRACT FROM AUTHOR]