Image generation technology for functional occlusal pits and fissures based on a conditional generative adversarial network.

The occlusal surfaces of natural teeth have complex features of functional pits and fissures. These morphological features directly affect the occlusal state of the upper and lower teeth. An image generation technology for functional occlusal pits and fissures is proposed to address the lack of local detailed crown surface features in existing dental restoration methods. First, tooth depth image datasets were constructed using an orthogonal projection method. Second, the optimization and improvement of the model parameters were guided by introducing the jaw position spatial constraint, the L1 loss and the perceptual loss functions. Finally, two image quality evaluation metrics were applied to evaluate the quality of the generated images, and deform the dental crown by using the generated occlusal pits and fissures as constraints to compare with expert data. The results showed that the images generated using the network constructed in this study had high quality, and the detailed pit and fissure features on the crown were effectively restored, with a standard deviation of 0.1802mm compared to the expert-designed tooth crown models.

Identifying Patients With Heart Failure Who Are Susceptible to De Novo Acute Kidney Injury: Machine Learning Approach.

BACKGROUND: Studies have shown that more than half of patients with heart failure (HF) with acute kidney injury (AKI) have newonset AKI, and renal function evaluation markers such as estimated glomerular filtration rate are usually not repeatedly tested during the hospitalization. As an independent risk factor, delayed AKI recognition has been shown to be associated with the adverse events of patients with HF, such as chronic kidney disease and death. OBJECTIVE: The aim of this study is to develop and assess of an unsupervised machine learning model that identifies patients with HF and normal renal function but who are susceptible to de novo AKI. METHODS: We analyzed an electronic health record data set that included 5075 patients admitted for HF with normal renal function, from which 2 phenogroups were categorized using an unsupervised machine learning algorithm called K-means clustering. We then determined whether the inferred phenogroup index had the potential to be an essential risk indicator by conducting survival analysis, AKI prediction, and the hazard ratio test. RESULTS: The AKI incidence rate in the generated phenogroup 2 was significantly higher than that in phenogroup 1 (group 1: 106/2823, 3.75%; group 2: 259/2252, 11.50%; P<.001). The survival rate of phenogroup 2 was consistently lower than that of phenogroup 1 (P<.005). According to logistic regression, the univariate model using the phenogroup index achieved promising performance in AKI prediction (sensitivity 0.710). The generated phenogroup index was also significant in serving as a risk indicator for AKI (hazard ratio 3.20, 95% CI 2.55-4.01). Consistent results were yielded by applying the proposed model on an external validation data set extracted from Medical Information Mart for Intensive Care (MIMIC) III pertaining to 1006 patients with HF and normal renal function. CONCLUSIONS: According to a machine learning analysis on electronic health record data, patients with HF who had normal renal function were clustered into separate phenogroups associated with different risk levels of de novo AKI. Our investigation suggests that using machine learning can facilitate patient phengrouping and stratification in clinical settings where the identification of high-risk patients has been challenging.

[Mortality and cause of death of hospitalized neonates in Weifang, China: a multicenter study]. / æ½åŠå¸‚ä½é™¢æ–°ç”Ÿå„¿ç— æ­»çŽ‡åŠæ­»äº¡åŽŸå› çš„å¤šä¸­å¿ƒç ”ç©¶.

OBJECTIVES: To study the changes in the mortality rate and cause of death of hospitalized neonates in grade A tertiary hospitals in Weifang City of Shandong Province during a 10-year period. METHODS: A retrospective analysis was performed on 461 neonates who died in three grade A tertiary hospitals in Weifang City of Shandong Province from January 1, 2012 to December 31, 2021. The related clinical data were collected to examine the changes of neonatal mortality with time, gestational age (GA) and birth weight (BW). The main causes of death of the neonates were compared between the first 5 years (2012-2016) and the last 5 years (2017-2021) in the period. RESULTS: A total of 43 037 neonates were admitted from 2012 to 2021, among whom 461 died, resulting in a mortality rate of 1.07%. The mortality rate in the last 5 years was significantly lower than that in the first 5 years [0.96% (211/22 059 vs 1.19% (250/20 978); P<0.05]. The mortality rate of neonates decreased with the increases in GA and BW (P<0.05). In the first 5 years, the top three main causes of neonatal death were respiratory distress syndrome (RDS), sepsis, and pneumorrhagia, while in the last 5 years, the top three causes were sepsis, pneumorrhagia, and RDS. The leading cause of death was severe asphyxia for the neonates with a GA of <26 weeks and a BW of <750 g in both the first and last 5 years. For the neonates with a GA of 26-<28 weeks, the leading cause of death changed from RDS in the first 5 years to pneumorrhagia in the last 5 years. For the neonates with a BW of 750-<1 000 g, the leading cause of death changed from pneumorrhagia in the first 5 years to RDS in the last 5 years. For the neonates with a GA of 28-<32 weeks and a BW of 1 000-<1 500 g, the leading cause of death was RDS in both the first and last 5 years. For the neonates with a GA of 32-<37 weeks and a BW of 1 500-<2 500 g, the leading cause of death changed from RDS in the first 5 years to sepsis in the last 5 years. The leading cause of death was sepsis for the neonates with a GA of 37-<42 weeks and a BW of 2 500-<4 000 g in both the first and last 5 years. CONCLUSIONS: The mortality rate of neonates in the grade A tertiary hospitals in Weifang City of Shandong Province has been decreasing in the past 10 years, and it decreases with the increases in GA and BW. Sepsis, RDS, and pneumorrhagia are the leading causes of neonatal death. The mortality rate caused by RDS decreases from the first 5 years to the last 5 years, while the mortality rate caused by sepsis or pneumorrhagia increases from the first 5 years to the last 5 years. Therefore, reducing the incidence rates of sepsis, RDS, and pneumorrhagia is the key to reducing neonatal mortality.