Early prediction of need for invasive mechanical ventilation in the neonatal intensive care unit using artificial intelligence and electronic health records: a clinical study.

BACKGROUND: Respiratory support is crucial for newborns with underdeveloped lung. The clinical outcomes of patients depend on the clinician's ability to recognize the status underlying the presented symptoms and signs. With the increasing number of high-risk infants, artificial intelligence (AI) should be considered as a tool for personalized neonatal care. Continuous monitoring of vital signs is essential in cardiorespiratory care. In this study, we developed deep learning (DL) prediction models for rapid and accurate detection of mechanical ventilation requirements in neonates using electronic health records (EHR). METHODS: We utilized data from the neonatal intensive care unit in a single center, collected between March 3, 2012, and March 4, 2022, including 1,394 patient records used for model development, consisting of 505 and 889 patients with and without invasive mechanical ventilation (IMV) support, respectively. The proposed model architecture includes feature embedding using feature-wise fully connected (FC) layers, followed by three bidirectional long short-term memory (LSTM) layers. RESULTS: A mean gestational age (GA) was 36.61 ± 3.25 weeks, and the mean birth weight was 2,734.01 ± 784.98 g. The IMV group had lower GA, birth weight, and longer hospitalization duration than the non-IMV group (P < 0.05). Our proposed model, tested on a dataset from March 4, 2019, to March 4, 2022. The mean AUROC of our proposed model for IMV support prediction performance demonstrated 0.861 (95%CI, 0.853-0.869). It is superior to conventional approaches, such as newborn early warning score systems (NEWS), Random Forest, and eXtreme gradient boosting (XGBoost) with 0.611 (95%CI, 0.600-0.622), 0.837 (95%CI, 0.828-0.845), and 0.0.831 (95%CI, 0.821-0.845), respectively. The highest AUPRC value is shown in the proposed model at 0.327 (95%CI, 0.308-0.347). The proposed model performed more accurate predictions as gestational age decreased. Additionally, the model exhibited the lowest alarm rate while maintaining the same sensitivity level. CONCLUSION: Deep learning approaches can help accurately standardize the prediction of invasive mechanical ventilation for neonatal patients and facilitate advanced neonatal care. The results of predictive, recall, and alarm performances of the proposed model outperformed the other models.

Deep learning-based computed tomographic image super-resolution via wavelet embedding.

Effort to realize high-resolution medical images have been made steadily. In particular, super resolution technology based on deep learning is making excellent achievement in computer vision recently. In this study, we developed a model that can dramatically increase the spatial resolution of medical images using deep learning technology, and we try to demonstrate the superiority of proposed model by analyzing it quantitatively. We simulated the computed tomography images with various detector pixel size and tried to restore the low-resolution image to high resolution image. We set the pixel size to 0.5, 0.8 and 1 mm2 for low resolution image and the high-resolution image, which were used for ground truth, was simulated with 0.25 mm2 pixel size. The deep learning model that we used was a fully convolution neural network based on residual structure. The result image demonstrated that proposed super resolution convolution neural network improve image resolution significantly. We also confirmed that PSNR and MTF was improved up to 38 % and 65% respectively. The quality of the prediction image is not significantly different depending on the quality of the input image. In addition, the proposed technique not only increases image resolution but also has some effect on noise reduction. In conclusion, we developed deep learning architectures for improving image resolution of computed tomography images. We quantitatively confirmed that the proposed technique effectively improves image resolution without distorting the anatomical structures.

Deep learning-based histopathological segmentation for whole slide images of colorectal cancer in a compressed domain.

Automatic pattern recognition using deep learning techniques has become increasingly important. Unfortunately, due to limited system memory, general preprocessing methods for high-resolution images in the spatial domain can lose important data information such as high-frequency information and the region of interest. To overcome these limitations, we propose an image segmentation approach in the compressed domain based on principal component analysis (PCA) and discrete wavelet transform (DWT). After inference for each tile using neural networks, a whole prediction image was reconstructed by wavelet weighted ensemble (WWE) based on inverse discrete wavelet transform (IDWT). The training and validation were performed using 351 colorectal biopsy specimens, which were pathologically confirmed by two pathologists. For 39 test datasets, the average Dice score, the pixel accuracy, and the Jaccard score were 0.804 ± 0.125, 0.957 ± 0.025, and 0.690 ± 0.174, respectively. We can train the networks for the high-resolution image with the large region of interest compared to the result in the low-resolution and the small region of interest in the spatial domain. The average Dice score, pixel accuracy, and Jaccard score are significantly increased by 2.7%, 0.9%, and 2.7%, respectively. We believe that our approach has great potential for accurate diagnosis.

GPU-accelerated 3D volumetric X-ray-induced acoustic computed tomography.

X-ray acoustic imaging is a hybrid biomedical imaging technique that can acoustically monitor X-ray absorption distribution in biological tissues through the X-ray induced acoustic effect. In this study, we developed a 3D volumetric X-ray-induced acoustic computed tomography (XACT) system with a portable pulsed X-ray source and an arc-shaped ultrasound array transducer. 3D volumetric XACT images are reconstructed via the back-projection algorithm, accelerated by a custom-developed graphics processing unit (GPU) software. Compared with a CPU-based software, the GPU software reconstructs an image over 40 times faster. We have successfully acquired 3D volumetric XACT images of various lead targets, and this work shows that the 3D volumetric XACT system can monitor a high-resolution X-ray dose distribution and image X-ray absorbing structures inside biological tissues.