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General General

Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning.

In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society

Brain tumor is one of the leading causes of cancer death. The high-grade brain tumors are easier to recurrent even after standard treatment. Therefore, developing a method to predict brain tumor recurrence location plays an important role in the treatment planning and it can potentially prolong patient's survival time. There is still little work to deal with this issue. In this paper, we present a deep learning-based brain tumor recurrence location prediction network. Since the dataset is usually small, we propose to use transfer learning to improve the prediction. We first train a multi-modal brain tumor segmentation network on the public dataset BraTS 2021. Then, the pre-trained encoder is transferred to our private dataset for extracting the rich semantic features. Following that, a multi-scale multi-channel feature fusion model and a nonlinear correlation learning module are developed to learn the effective features. The correlation between multi-channel features is modeled by a nonlinear equation. To measure the similarity between the distributions of original features of one modality and the estimated correlated features of another modality, we propose to use Kullback-Leibler divergence. Based on this divergence, a correlation loss function is designed to maximize the similarity between the two feature distributions. Finally, two decoders are constructed to jointly segment the present brain tumor and predict its future tumor recurrence location. To the best of our knowledge, this is the first work that can segment the present tumor and at the same time predict future tumor recurrence location, making the treatment planning more efficient and precise. The experimental results demonstrated the effectiveness of our proposed method to predict the brain tumor recurrence location from the limited dataset.

Zhou Tongxue, Noeuveglise Alexandra, Modzelewski Romain, Ghazouani Fethi, Thureau Sébastien, Fontanilles Maxime, Ruan Su

2023-Mar-16

Brain tumor recurrence, Correlation learning, Deep learning, Location prediction, Multi-modal fusion

Radiology Radiology

BTMF-GAN: A multi-modal MRI fusion generative adversarial network for brain tumors.

In Computers in biology and medicine

Image fusion techniques have been widely used for multi-modal medical image fusion tasks. Most existing methods aim to improve the overall quality of the fused image and do not focus on the more important textural details and contrast between the tissues of the lesion in the regions of interest (ROIs). This can lead to the distortion of important tumor ROIs information and thus limits the applicability of the fused images in clinical practice. To improve the fusion quality of ROIs relevant to medical implications, we propose a multi-modal MRI fusion generative adversarial network (BTMF-GAN) for the task of multi-modal MRI fusion of brain tumors. Unlike existing deep learning approaches which focus on improving the global quality of the fused image, the proposed BTMF-GAN aims to achieve a balance between tissue details and structural contrasts in brain tumor, which is the region of interest crucial to many medical applications. Specifically, we employ a generator with a U-shaped nested structure and residual U-blocks (RSU) to enhance multi-scale feature extraction. To enhance and recalibrate features of the encoder, the multi-perceptual field adaptive transformer feature enhancement module (MRF-ATFE) is used between the encoder and the decoder instead of a skip connection. To increase contrast between tumor tissues of the fused image, a mask-part block is introduced to fragment the source image and the fused image, based on which, we propose a novel salient loss function. Qualitative and quantitative analysis of the results on the public and clinical datasets demonstrate the superiority of the proposed approach to many other commonly used fusion methods.

Liu Xiao, Chen Hongyi, Yao Chong, Xiang Rui, Zhou Kun, Du Peng, Liu Weifan, Liu Jie, Yu Zekuan

2023-Mar-09

Adaptive transformer, Image fusion, Multi-modal MRI, Salient loss

Public Health Public Health

Intelligent decision support in medical triage: are people robust to biased advice?

In Journal of public health (Oxford, England)

BACKGROUND : Intelligent artificial agents ('agents') have emerged in various domains of human society (healthcare, legal, social). Since using intelligent agents can lead to biases, a common proposed solution is to keep the human in the loop. Will this be enough to ensure unbiased decision making?

METHODS : To address this question, an experimental testbed was developed in which a human participant and an agent collaboratively conduct triage on patients during a pandemic crisis. The agent uses data to support the human by providing advice and extra information about the patients. In one condition, the agent provided sound advice; the agent in the other condition gave biased advice. The research question was whether participants neutralized bias from the biased artificial agent.

RESULTS : Although it was an exploratory study, the data suggest that human participants may not be sufficiently in control to correct the agent's bias.

CONCLUSIONS : This research shows how important it is to design and test for human control in concrete human-machine collaboration contexts. It suggests that insufficient human control can potentially result in people being unable to detect biases in machines and thus unable to prevent machine biases from affecting decisions.

van der Stigchel Birgit, van den Bosch Karel, van Diggelen Jurriaan, Haselager Pim

2023-Mar-20

emergency care, ethics, health intelligence

Surgery Surgery

The Role of Scribes in Orthopaedics.

In JBJS reviews

» : The rapid increase in the use of electronic medical records (EMRs) has led to some unintended consequences that negatively affect physicians and their patients.

» : The use of medical scribes may serve as a possible solution to some of the EMR-related concerns.

» : Research has demonstrated an overall positive impact of having scribes on both physician and patient well-being, safety, and satisfaction.

» : Adaptation of advances in technology, including remote and asynchronous scribing, use of face-mounted devices, voice recognition software, and applications of artificial intelligence may address some of the barriers to more traditional in-person scribes.

Lam Michelle, Sabharwal Sanjeev

2023-Mar-01

General General

Mechanism of assembly of type 4 filaments: everything you always wanted to know (but were afraid to ask).

In Microbiology (Reading, England)

Type 4 filaments (T4F) are a superfamily of filamentous nanomachines - virtually ubiquitous in prokaryotes and functionally versatile - of which type 4 pili (T4P) are the defining member. T4F are polymers of type 4 pilins, assembled by conserved multi-protein machineries. They have long been an important topic for research because they are key virulence factors in numerous bacterial pathogens. Our poor understanding of the molecular mechanisms of T4F assembly is a serious hindrance to the design of anti-T4F therapeutics. This review attempts to shed light on the fundamental mechanistic principles at play in T4F assembly by focusing on similarities rather than differences between several (mostly bacterial) T4F. This holistic approach, complemented by the revolutionary ability of artificial intelligence to predict protein structures, led to an intriguing mechanistic model of T4F assembly.

Pelicic Vladimir

2023-Mar

nanomachines, pili, pilin, type 2 secretion systems, type 4 filaments, type 4 pili

Internal Medicine Internal Medicine

Predicting outcomes of continuous renal replacement therapy using body composition monitoring: a deep-learning approach.

In Scientific reports ; h5-index 158.0

Fluid balance is a critical prognostic factor for patients with severe acute kidney injury (AKI) requiring continuous renal replacement therapy (CRRT). This study evaluated whether repeated fluid balance monitoring could improve prognosis in this clinical population. This was a multicenter retrospective study that included 784 patients (mean age, 67.8 years; males, 66.4%) with severe AKI requiring CRRT during 2017-2019 who were treated in eight tertiary hospitals in Korea. Sequential changes in total body water were compared between patients who died (event group) and those who survived (control group) using mixed-effects linear regression analyses. The performance of various machine learning methods, including recurrent neural networks, was compared to that of existing prognostic clinical scores. After adjusting for confounding factors, a marginal benefit of fluid balance was identified for the control group compared to that for the event group (p = 0.074). The deep-learning model using a recurrent neural network with an autoencoder and including fluid balance monitoring provided the best differentiation between the groups (area under the curve, 0.793) compared to 0.604 and 0.606 for SOFA and APACHE II scores, respectively. Our prognostic, deep-learning model underlines the importance of fluid balance monitoring for prognosis assessment among patients receiving CRRT.

Yoo Kyung Don, Noh Junhyug, Bae Wonho, An Jung Nam, Oh Hyung Jung, Rhee Harin, Seong Eun Young, Baek Seon Ha, Ahn Shin Young, Cho Jang-Hee, Kim Dong Ki, Ryu Dong-Ryeol, Kim Sejoong, Lim Chun Soo, Lee Jung Pyo

2023-Mar-21