Accuracy of bone age assessment system based on deep learning in children with abnormal growth and development / 中华放射学杂志

Objective:To explore the accuracy of artificial intelligence (AI) system based on deep learning in evaluating bone age of children with abnormal growth and development.Methods:The positive X-ray films of the left wrist of children with abnormal growth and development who were treated at the Affiliated Hospital of Guizhou Medical University from January 2020 to December 2021 were collected retrospectively. A total of 717 children were collected, including 266 males and 451 females, aged 2-18 (11±3) years. Based on Tanner Whitehouse 3 (TW 3)-RUS (radius, ulna, short bone) and TW3-Carpal (carpal bone) method, bone age was measured by 3 senior radiologists, and the mean value was taken as reference standard. The bone ages were independently evaluated by the AI system (Dr.Wise bone age prediction software) and two junior radiologists (physicians 1 and 2). The accuracy within 0.5 year, the accuracy within 1 year, the mean absolute error (MAE) and the root mean square error (RMSE) between the evaluation results and the reference standard were analyzed. Paired sample t-test was used to compare MAE between AI system and junior physicians. Intraclass correlation coefficient (ICC) was used to evaluate the consistency between AI system, junior physician and reference standard. The Bland-Altman diagram was drawn and the 95% consistency limit was calculated between AI system and reference standard. Results:For TW3-RUS bone age, compared with the reference standard, the accuracy within 0.5 year of AI system, physician 1 and physician 2 was 75.3% (540/717), 62.1% (445/717) and 66.2% (475/717), respectively. The accuracy within 1 year was 96.9% (695/717), 86.3% (619/717) and 89.1% (639/717), respectively. MAE was 0.360, 0.565 and 0.496 years, and RMSE was 0.469, 0.634 and 0.572 years, respectively. For TW3-Carpal bone age, compared with the reference standard, the accuracy within 0.5 year of AI system, physician 1 and physician 2 was 80.9% (580/717), 65.1% (467/717) and 71.7% (514/717), respectively. The accuracy within 1 year was 96.0% (688/717), 87.3% (626/717) and 90.4% (648/717), respectively. MAE was 0.330, 0.527 and 0.455 years, and RMSE was 0.458, 0.612, 0.538 years, respectively. Based on TW3-RUS and TW3-Carpal bone age, the MAE of AI system were lower than those of physician 1 and physician 2, and the differences were statistically significant ( P all<0.001). The evaluation results of AI, physician 1 and physician 2 were in good agreement with the reference standard (ICC all>0.950). The Bland-Altman analysis showed that the 95% agreement limits of AI system for assessing TW3-RUS and TW3-Carpal bone age were -0.75-1.02 years and-0.86-0.91 years, respectively. Conclusion:The accuracy of AI system in evaluating the bone age of children with abnormal growth and development is close to that of senior doctors, better than that of junior doctors, and in good agreement with senior doctors.

Purification and characterization of a low-temperature hydroxylamine oxidase from heterotrophic nitrifier Acinetobacter sp. Y16.

OBJECTIVE: To purify a low-temperature hydroxylamine oxidase (HAO) from a heterotrophic nitrifying bacterium Acinetobacter sp. Y16 and investigate the enzyme property. METHODS: A HAO was purified by an anion-exchange and gel-filtration chromatography from strain Y16. The purity and molecular mass were determined by RP-HPLC and SDS-PAGE. The HAO activity was detected by monitoring the reduction of potassium ferricyanide using hydroxylamine as substrate and ferricyanide as electron acceptor. The partial amino acid sequence was determined by mass spectrometry. RESULTS: The low-temperature HAO with a molecular mass of 61 kDa was purified from strain Y16 by an anion-exchange and gel-filtration chromatography. The enzyme exhibited an ability to oxidize hydroxylamine in wide temperature range (4-40 °C) in vitro using hydroxylamine as substrate and ferricyanide as electron acceptor. It was stable in the temperature range of 4 to 15 °C and pH range of 6.0 to 8.5 with less than 30% change in its activity. The optimal temperature and pH were 15 °C and 7.5, respectively. Three peptides were determined by mass spectrometry which were shown to be not identical to other reported HAOs. CONCLUSION: This is the first study to purify a low-temperature HAO from a heterotrophic nitrifier Acinetobacter sp. It differs from other reported HAOs in molecular mass and enzyme properties. The findings of the present study have suggested that the strain Y16 passes through a hydroxylamine-oxidizing process catalyzed by a low-temperature HAO for ammonium removal.

Risk factors on the recurrence of ischemic stroke and the establishment of a Cox' s regression model / 中华流行病学杂志

Objective To investigate the risk factors and establish the Cox' s regression model on the recurrence of ischemic stroke. Methods We retrospectively reviewed consecutive patients with ischemic stroke admitted to the Neurology Department of the Hebei United University Affiliated Hospital between January 1,2008 and December 31,2009. Cases had been followed since the onset of ischemic stroke. The follow-up program was finished in June 30, 2010. Kaplan-Meier methods were used to describe the recurrence rate. Monovariant and multivariate Cox' s proportional hazard regression model were used to analyze the risk factors associated to the episodes of recurrence.And then, a recurrence model was set up. Results During the period of follow-up program, 79 cases were relapsed,with the recurrence rates as 12.75% in one year and 18.87% in two years. Monovariant and multivariate Cox' s proportional hazard regression model showed that the independent risk factors that were associated with the recurrence appeared to be age (X1)(RR=1.025,95% CI: 1.003-1.048),history of hypertension (X2) (RR= 1.976, 95% CI: 1.014-3.851), history of family strokes (X3) (RR=2.647,95%CI: 1.175-5.961), total cholesterol amount (X4) (RR= 1.485,95%CI: 1.214-1.817), ESRS total scores (X5) (RR= 1.327,95%CI: 1.057-1.666) and progression of the disease (X6) (RR= 1.889,95%CI: 1.123-3.178). Personal prognosis index (PI) of the recurrence model was as follows: PI=0.025X1 + 0.681X2+ 0.973X3 + 0.395X4+ 0.283X5 + 0.636X6. The smaller the personal prognosis index was, the lower the recurrence risk appeared, while the bigger the personal prognosis index was, the higher the recurrence risk appeared. Conclusion Age, history of hypertension, total cholesterol amount, total scores of ESRS, together with the disease progression were the independent risk factors associated with the recurrence episodes of ischemic stroke. Both recurrence model and the personal prognosis index equation were successful constructed.