2D/3D ultrasound diagnosis of pediatric distal radius fractures by human readers vs artificial intelligence.

Wrist trauma is common in children and generally requires radiography for exclusion of fractures, subjecting children to radiation and long wait times in the emergency department. Ultrasound (US) has potential to be a safer, faster diagnostic tool. This study aimed to determine how reliably US could detect distal radius fractures in children, to contrast the accuracy of 2DUS to 3DUS, and to assess the utility of artificial intelligence for image interpretation. 127 children were scanned with 2DUS and 3DUS on the affected wrist. US scans were then read by 7 blinded human readers and an AI model. With radiographs used as the gold standard, expert human readers obtained a mean sensitivity of 0.97 and 0.98 for 2DUS and 3DUS respectively. The AI model sensitivity was 0.91 and 1.00 for 2DUS and 3DUS respectively. Study data suggests that 2DUS is comparable to 3DUS and AI diagnosis is comparable to human experts.

Self-supervised-RCNN for medical image segmentation with limited data annotation.

Many successful methods developed for medical image analysis based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To overcome the problem of learning with limited labeled medical image data, an alternative deep learning training strategy based on self-supervised pretraining on unlabeled imaging data is proposed in this work. For the pretraining, different distortions are arbitrarily applied to random areas of unlabeled images. Next, a Mask-RCNN architecture is trained to localize the distortion location and recover the original image pixels. This pretrained model is assumed to gain knowledge of the relevant texture in the images from the self-supervised pretraining on unlabeled imaging data. This provides a good basis for fine-tuning the model to segment the structure of interest using a limited amount of labeled training data. The effectiveness of the proposed method in different pretraining and fine-tuning scenarios was evaluated based on the Osteoarthritis Initiative dataset with the aim of segmenting effusions in MRI datasets of the knee. Applying the proposed self-supervised pretraining method improved the Dice score by up to 18% compared to training the models using only the limited annotated data. The proposed self-supervised learning approach can be applied to many other medical image analysis tasks including anomaly detection, segmentation, and classification.

Domain-aware contrastive learning for ultrasound hip image analysis.

Early diagnosis of Developmental Dysplasia of Hip (DDH) using ultrasound can result in simpler and more effective treatment options. Handheld ultrasound probes are ideally suited for such screening due to their low cost and portability. However, images from the pocket-sized probes are of lower quality than conventional probes. Image quality can be enhanced by image translation techniques that generate a pseudo-image mimicking the image quality of conventional probes. This can also help in generalizing the performance of AI-based automatic interpretation techniques to multiple probes. We develop a new domain-aware contrastive unpaired translation (D-CUT) technique for translating between images acquired from different ultrasound probes. Our approach embeds a Bone Probability Map (BPM) as part of the loss function which enforces higher structural similarity around bony regions in the image. Using the D-CUT model we translated 575 images acquired from a Philips Lumify handheld probe to generate pseudo-3D ultrasound (3DUS) images similar (Fréchet Inception Distance = 92) to those acquired from a conventional ultrasound probe (Philips iU22). The pseudo-3DUS images showed high structural similarity (SSIM = 0.68, Cosine Similarity = 0.65) with the original images and improved the contrast around the bony regions. This study establishes the feasibility of using D-CUT to improve the quality of data acquired from handheld ultrasound probes. Among other potential applications, clinical use of this tool could result in wider use of ultrasound for DDH screening programs.

Prediction Model of Rice Seedling Growth and Rhizosphere Fertility Based on the Improved Elman Neural Network.

Rice developing prognostication is a key part of precise agricultural management, and its advancement is an intricate course of events involving the interplay of breed and environmental element. The traditional research method is based on data analysis of rice growth prediction modeling, mining the concealed rapport between rice productivity and circumstance element, for instance, weather, sunlight, and water, and then predicting its yield and analyzing the complex rapport between the circumstance element and growth in every developing phase. In this dissertation, the improved ElmanNN is accustomed to establish a prediction model, and the ElmanNN is accustomed to determine the rapport between the circumstance element and growth in every developing phase simultaneously so as to avoid the arithmetic falling into local optimum easily. In this dissertation, the improved genetic arithmetic is accustomed to optimize the initial weight and threshold of Elman neural network, and the range of weight value multitudinous layers in the mould are obtained by training the network with samples that have been tested in the last few years. Finally, the rapport between growth and yield in six different periods is independently modeled, and the training samples are build up separately one by one based on physiological parameters and environmental indicators of rice at every level. The experiments show that the accuracy for the prediction model in the light of the improved ElmanNN has been beneficial.

Impacts of environmental factors on the whole microbial communities in the rhizosphere of a metal-tolerant plant: Elsholtzia haichowensis Sun.

Rhizospheric microbes play important roles in plant growth and heavy metals (HMs) transformation, possessing great potential for the successful phytoremediation of environmental pollutants. In the present study, the rhizosphere of Elsholtzia haichowensis Sun was comprehensively studied to uncover the influence of environmental factors (EFs) on the whole microbial communities including bacteria, fungi and archaea, via quantitative polymerase chain reaction (qPCR) and high-throughput sequencing. By analyzing molecular ecological network and multivariate regression trees (MRT), we evaluated the distinct impacts of 37 EFs on soil microbial community. Of them, soil pH, HMs, soil texture and nitrogen were identified as the most influencing factors, and their roles varied across different domains. Soil pH was the main environmental variable on archaeal and bacterial community but not fungi, explaining 25.7%, 46.5% and 40.7% variation of bacterial taxonomic composition, archaeal taxonomic composition and a-diversity, respectively. HMs showed important roles in driving the whole microbial community and explained the major variation in different domains. Nitrogen (NH4-N, NO3-N, NO2-N and TN) explained 47.3% variation of microbial population composition and 15.9% of archaeal taxonomic composition, demonstrating its influence in structuring the rhizospheric microbiome, particularly archaeal and bacterial community. Soil texture accounted for 10.2% variation of population composition, 28.9% of fungal taxonomic composition, 19.2% of fungal a-diversity and 7.8% of archaeal a-diversity. Rhizosphere only showed strong impacts on fungi and bacteria, accounting for 14.7% and 4.9% variation of fungal taxonomic composition and bacterial a-diversity. Spatial distance had stronger influence on bacteria and archaea than fungi, but not as significant as other EFs. For the first time, our study provides a complete insight into key influential EFs on rhizospheric microbes and how their roles vary across microbial domains, giving a hand for understanding the construction of microbial communities in rhizosphere.