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Objective:To explore the application value of artificial intelligence (AI) model based on deep learning in breast nodules classification of Breast Imaging Reporting and Data System of ultrasound (BI-RADS-US).Methods:The ultrasound images of 2 426 breast nodules from 1 558 female patients with breast diseases at Beijing Tongren Hospital, Capital Medical University between December 2006 and December 2019 were collected . The image data sets were divided into training (63%), verification (7%), and test (30%) subsets for the construction of AI model. The diagnostic efficiencies of AI model, doctors' arbitration results and doctors' diagnosis with or without AI model assistance were analyzed by using receiver operating characteristic (ROC) curve. The Cohen weighted Kappa statistic was used to compare the consistency of BI-RADS-US classification among 5 ultrasound doctors' diagnosis with or without AI model assistance. And the changes of BI-RADS-US classification were analyzed before and after each doctor adopted AI model assistance.Results:The differences in diagnostic efficiencies of AI model, doctors' arbitration results and doctors' diagnosis with or without AI model assistance were statistically significant (all P > 0.05). The consistency among 5 ultrasound doctors was improved due to AI model assistance and Kappa value was increased from 0.433 (category 3), 0.600 (category 4a), 0.614 (category 4b), 0.570 (category 4c) and 0.495 (category 5) to 0.812, 0.704, 0.823, 0.690 and 0.509 (all P < 0.05), respectively. The upgrade and downgrade of BI-RADS-US classification occurred in 5 doctors after the classification of AI model assistance. Downgrade from category 4 to 3 in benign nodules of 56.6% (47/76) and upgrade from category 4 to 5 in malignant nodules of 69.4% (34/49) were mostly observed. Conclusions:AI-assisted BI-RADS-US classification can effectively improve the consistency of classification among the doctors without reducing the diagnostic efficiency. AI model shows clinical values in reducing unnecessary biopsy of partial benign lesions and increasing diagnostic accuracy of partial malignant lesions through the adjustment of breast nodule classification.
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Objective@#To explore the application value of the convolutional neural network (CNN)-based artificial intelligence-assisted diagnosis model in the ultrasound differentiation diagnosis of benign and malignant breast nodules.@*Methods@#A total of 7 334 ultrasound images from 1 351 patients with breast nodules including 807 benign cases and 544 malignant cases were retrieved by using the CNN-based artificial intelligence-assisted diagnosis model from Beijing Tongren Hospital of Capital Medical University ultrasound images database between December 2006 and July 2017. The study included training subset (6 162 images), verification subset (555 images), and test subset (617 images), which were performed in the artificial intelligence-assisted diagnosis model. The outcome results of test subset in diagnosis model were compared with the pathological results. The sensitivity, specificity and accuracy of the artificial intelligence-assisted diagnosis model were calculated.@*Results@#After the test of 617 images, the model diagnostic results could be automatically output with a rectangular frame indicating the nodule position, benign and malignant diagnosis, benign and malignant probability values. The diagnosis time was approximately 4 seconds for each nodule. The sensitivity, specificity and accuracy of the diagnostic model in differentiating benign and malignant breast nodules were 84.1%, 95.0% and 91.2% , respectively.@*Conclusion@#The CNN-based artificial intelligence-assisted diagnosis model has satisfactory results in the differentiation diagnosis of the benign breast nodules and the malignant ones, which indicating the promising application prospect.
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Objective To explore the application value of the convolutional neural network (CNN)-based artificial intelligence-assisted diagnosis model in the ultrasound differentiation diagnosis of benign and malignant breast nodules. Methods A total of 7334 ultrasound images from 1351 patients with breast nodules including 807 benign cases and 544 malignant cases were retrieved by using the CNN-based artificial intelligence-assisted diagnosis model from Beijing Tongren Hospital of Capital Medical University ultrasound images database between December 2006 and July 2017. The study included training subset (6162 images), verification subset (555 images), and test subset (617 images), which were performed in the artificial intelligence-assisted diagnosis model. The outcome results of test subset in diagnosis model were compared with the pathological results. The sensitivity, specificity and accuracy of the artificial intelligence-assisted diagnosis model were calculated. Results After the test of 617 images, the model diagnostic results could be automatically output with a rectangular frame indicating the nodule position, benign and malignant diagnosis, benign and malignant probability values. The diagnosis time was approximately 4 seconds for each nodule. The sensitivity, specificity and accuracy of the diagnostic model in differentiating benign and malignant breast nodules were 84.1％, 95.0％ and 91.2％ , respectively. Conclusion The CNN-based artificial intelligence-assisted diagnosis model has satisfactory results in the differentiation diagnosis of the benign breast nodules and the malignant ones, which indicating the promising application prospect.
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Objective To evaluate quantitative parameters of contrast-enhanced transrectal ultrasonography for differential diagnosis of prostate nodules in peripheral zone.Methods Forty-seven patients suspected of prostate cancer for peripheral zone nodules on ultrasonographic imaging were enrolled in this study.Time intensity curves of contrast-enhanced ultrasound were analyzed in all patients.Results The full-width at half maximum (FWHM) of malignant lesions in peripheral zone was shorter than that of adjacent peripheral zone (47.1 s±21.1 s vs 74.2 s±29.7 s, P=0.01).The peak intensity (PI) of benign nodules in peripheral zone was lower than that of adjacent peripheral zone 11.9 dB±7.7 dB vs 17.5 dB±4.5 dB, P=0.02).Conclusions It is helpful for differentiation diagnosis of peripheral zone nodule through analyzing FWHM and PI on contrast-enhanced transrectal ultmsonography imaging.
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OBJECTIVE@#To evaluate the therapeutic effect of multi-plane operations in treating obstructive sleep apnea-hypopnea syndrome (OSAHS).@*METHOD@#One hundred and fifteen patients with OSAHS diagnosed by polysomnography were treated with uvuplopalatopharyngoplasty. Eighteen of them were treated combining with nasal septal construction. Twenty six patients also received nasal septal construction and partial inferior turbinectomy. One patients also received Genioglossus advancement and partial resection of corpus linguae. Five patients also received partial resection of corpus linguae. Some patients with the nasal disease and/or the lingual hypertrophy; AHI > 40 and/or BMI > 30 are received tracheotomy before general anaesthesia.@*RESULT@#According to the postoperative follow-up 43 patients, were cured, 46 patients were improved, 26 patients were invalid, the effective power was 77.4%.@*CONCLUSION@#The operative effective power were increased by multi-plane operations in OSAHS patients. The serious complication were prevented through tracheotomy before general anaesthesia in multi-plane operations of severe OSAHS.