Background: Morphological analysis of bone marrow cells is considered as the gold standard for the diagnosis of leukemia. However, due to the diverse morphology of bone marrow cells, extensive experience and patience are needed for morphological examination. automatic diagnosis system through the comprehensive application of image analysis and pattern recognition technology is urgently needed to reduce work intensity, error probability and improves work efficiency. Methods: In this article, we establish a new morphological diagnosis system for bone marrow cell detection based on the deep learning object detection framework. The model is based on the Faster Region-Convolutional Neural Network (R-CNN), a classical object detection model. The system automatically detects bone marrow cells and determines their types. As specimens have severe long-tail distribution, i.e., the frequency of different types of cells varies dramatically, we proposed a general score ranking loss to solve such a problem. The general score ranking loss considers the ranking relationship between positive and negative samples and optimizes the positive sample with a higher classification probability value.Results: We verified this system with 70 bone marrow specimens of leukemia patients, which proved that it can realize intelligent recognition with high efficiency. The software is finally integrated into the microscope system to build an augmented reality system.Conclusions: Clinical tests show that the response speed of the newly developed diagnostic system is faster than that of trained diagnostic experts.

Summary The lack of bike lane access has been a proven risk factor for childhood obesity due to its role in discouraging healthy lifestyles. However, there has not been a systematic review of this important association in the existing literature. This study aims to fill this gap. A literature search was conducted in the Cochrane Library, PubMed, Embase, and Web of Science for studies published from 1 January 2019 onwards that examined the association between bike lane access and weight‐related behaviours and outcomes among children aged <18 years. A total of 21 studies were included in this systematic review. Among them, most of the studies showed that bike lane access was significantly associated with children and adolescents' physical activity (PA), whereas only two studies showed a negative association. Meta‐analysis also supported these findings and showed that bike lane access was significantly associated with children and adolescents' PA (odds ratio [OR] = 1.57, 95% confidence interval [CI]: 1.37–1.81). Additionally, we reviewed how bike lane characteristics and microenvironment variables such as children and adolescents' choice of bicycle travel mode, the degree of separation of cycle path, cycle path unevenness, and street maintenance were associated with adolescents' preferences and intention to cycle. This systematic review and meta‐analysis strongly suggests that bike lane access is associated with children and adolescents' PA. Nonetheless, it was difficult to draw a conclusion on the association between bike lane access and weight‐related outcomes.