Establishment and Initial Application of YOLO-V5 Deep Learning Prediction Model for Accurate Identification of Drug Packaging in Outpatient and Emergency Pharmacies / 医药导报

Objective To develop an accurate deep learning prediction model of YOLO-V5 capable of accurately iden-tifying medication packaging boxes in outpatient and emergency pharmacies,aiming to assist pharmacists in achieving"zero dis-pensing error".Methods A total of 2 560 images of packaging boxes from 136 different drugs were collected and labeled to form the deep learning dataset.The dataset was split into training and validation sets at a ratio of 4∶1.YOLO-V5 deep-learning algorithm was employed for training the data using images from our dataset(train epochs:500,batch size:4,learning rate:0.01).The values of the precision(Pr)and mean average precision(mAP)were used as measures for model performance evaluation.Results The Pr of the four sub-models of YOLO-V5 in the training set all reached 1.00.The mAP_0.5 of YOLO-V5x was 0.95,which was higher than those of YOLO-V5s(0.94),YOLO-V5l(0.94),and YOLO-V5m(0.94).The mAP_0.5:0.95 of YOLO-V5l and YOLO-V5x were 0.85 which were higher than those of YOLO-V5s(0.84)and YOLO-V5m(0.84).Training time and model size were 82.56 hours and 166.00MB for YOLO-V5x which were the highest among the four models.The speed of detection in one im-age was 11ms for YOLO-V5s which was the fastest among the four models.Conclusion YOLO-V5 can accurately identify the packaging of drugs in outpatient and emergency pharmacies.Implementing an artificial-intelligence-assisted drug dispensation sys-tem is feasible for pharmacists to achieve"zero dispensing error".

The value of MobileNet in classification of bedside chest radiograph / 中华放射学杂志

Objective:To investigate the value of a deep learning method based on MobileNet in classification of bedside chest radiograph and improvement of the work efficiency.Methods:A total of 6, 320 bedside chest radiographs from January 2017 to December 2022 in the Second Peoples′ Hospital of Changzhou were retrospectively collected. The included cases were divided into normal group (885 images), pneumonia group (1 927 images), pleural effusion group (373 images), and pneumonia with pleural effusion group (3 135 images). Three hundred and fifty images were selected as a validation set, while the remaining images were divided into a train set (4 775 images) and a test set (1 195 images) using simple randomization, by 8∶2 ratio. Two lightweight convolutional neural network models (MobileNetV1 and MobileNetV2) were used to construct a bedside chest radiograph classification model, based on which two fine-tuning strategies were designed. Four models were generated namely MobileNetV1_False (V1_False), MobileNetV1_True (V1_True), MobileNetV2_False (V2_False) and MobileNetV2_True (V2_True). In the first stage, a binary classification model was established to divide the images into normal and lesion groups; then a four-class classification model was established in the second stage, with which the images were divided into four groups: normal, pneumonia, pleural effusion and pneumonia with pleural effusion. Metrics for model performance evaluation including accuracy (Ac), precision (Pr), recall rate (Rc), F1 score (F1) and area under the receiver operating characteristic curve (AUC) were calculated.Results:In both the first and second stages, V1_True and V2_True had higher Ac, Pr, Rc, and F1 than V1_False and V2_False in both the training set and validation set; and the V1_True model outperformed the other three models in classification. The classification Ac of the V1_True model in the validation set was higher than that of radiologists in the first stage [95.71% (335/350) vs. 90.29% (316/350)] and in the second stage [93.43% (327/350) vs. 87.14% (305/350)]. The recognition time of V1_True model′s in the validation set of 350 bedside chest radiographs was significantly less than that of the radiologists (mean: 17 s vs. 300 min).Conclusions:V1_True is an optimal MobileNet model for classifying bedside chest radiographs. The application of this model in clinical practice may help to accurately identify the information of lung lesions from bedside chest radiographs in time, and may improve the work efficiency in the radiology department.

Prospective study on the value of CARE kV technique in reducing the radiation dose in adult chest CT imaging / 中华放射医学与防护杂志

Objective To explore the application of CARE kV technique in the adult chest CT and the value of reducing radiation dose.Methods Sixty-nine patients were divided into two groups by random number generators:group A(39 cases) and group B(30 cases).Group A was examined by using CARE kV technique and group B was examined at routine 120 kV.CT dose index(CTDIvol),dose length product (DLP) and effective dose (E) were compared between the two groups,and analyzed the correlation between tube voltage selection and patient body mass index (BMI) of group A was analyzed.Results The average CTDIvol [(11.00 ± 3.89) mGy],DLP[(294.05 ± 91.17) mGy·cm] and E[(4.12 ± 1.28) mSv] of group A were lower than those of group B (16.64 ± 1.20) mGy,[(475.99 ± 41.16) mGy · cm],[(6.66 ±0.58) mSv].With statistically significant difference (t =-7.653,-10.151,-10.150,P ＜ 0.05).Compared with routine 120 kV technique (group B),the CARE kV technique (group A) could reduce the total radiation dose about 38.14％.Compared obese patients(BMI≥28 kg/m2) with non-obese patients in group A and B,the mean E of non-obese patients was lower than that of obese patients in group A,which reduced the total E about 31.74％ (t =4.322,P ＜0.05),while E in group B was no significant different between non-obese patients and obese patients.Conclusions In adult chest CT,CARE kV technique can select optimum scanning voltage automatically according to the patients with different BMI and anatomical regions,which can reduce the overall radiation dose while maintaining image quality.

Vogt-Koyanagi-Harada syndrome: 5 cases report with clinical and neuroimaging features / 中华神经科杂志

Objective To investigate the clinical and neuroimaging features of Vogt-KoyanagiHarada syndrome ( VKH ).Methods Cerebrospinal fluid ( CSF ), neuroimaging examination, clinical manifestation and pharmacotherapy features were investigated in 5 patients diagnosed as VKH. ResultsAll 5 patients were diagnosed as uveitis in the early stage of disease.All patients suffered “ headache”.Meningeal irritation sign was appeared in 3 cases. The MRI enhanced scan of all 5 cases showed abnormal enhancement of meninges. CSF examination showed increased leukocyte number ((4--196) × 106/L). All patients were alleviatedwith combination therapyof high dose of steroid with cyclophosphamide.ConclusionsVKH is a systemic disease that usually involving the uvea, central nervous system, internal ear and the skin. MRI and CSF examination are valuable for diagnosis. High dose of steroid combined with cyclophosphamide is an effective therapeutic strategy.