Neural Comput. 2024 Feb 28:1-59. doi: 10.1162/neco_a_01642. Online ahead of print.

ABSTRACT

The free energy principle and its corollary, the active inference framework, serve as theoretical foundations in the domain of neuroscience, explaining the genesis of intelligent behavior. This principle states that the processes of perception, learning, and decision making-within an agent-are all driven by the objective of "minimizing free energy," evincing the following behaviors: learning and employing a generative model of the environment to interpret observations, thereby achieving perception, and selecting actions to maintain a stable preferred state and minimize the uncertainty about the environment, thereby achieving decision making. This fundamental principle can be used to explain how the brain processes perceptual information, learns about the environment, and selects actions. Two pivotal tenets are that the agent employs a generative model for perception and planning and that interaction with the world (and other agents) enhances the performance of the generative model and augments perception. With the evolution of control theory and deep learning tools, agents based on the FEP have been instantiated in various ways across different domains, guiding the design of a multitude of generative models and decision-making algorithms. This letter first introduces the basic concepts of the FEP, followed by its historical development and connections with other theories of intelligence, and then delves into the specific application of the FEP to perception and decision making, encompassing both low-dimensional simple situations and high-dimensional complex situations. It compares the FEP with model-based reinforcement learning to show that the FEP provides a better objective function. We illustrate this using numerical studies of Dreamer3 by adding expected information gain into the standard objective function. In a complementary fashion, existing reinforcement learning, and deep learning algorithms can also help implement the FEP-based agents. Finally, we discuss the various capabilities that agents need to possess in complex environments and state that the FEP can aid agents in acquiring these capabilities.

PMID:38457757 | DOI:10.1162/neco_a_01642

Neural Comput. 2024 Feb 28:1-15. doi: 10.1162/neco_a_01653. Online ahead of print.

ABSTRACT

Recent advancements in deep learning have achieved significant progress by increasing the number of parameters in a given model. However, this comes at the cost of computing resources, prompting researchers to explore model compression techniques that reduce the number of parameters while maintaining or even improving performance. Convolutional neural networks (CNN) have been recognized as more efficient and effective than fully connected (FC) networks. We propose a column row convolutional neural network (CRCNN) in this letter that applies 1D convolution to image data, significantly reducing the number of learning parameters and operational steps. The CRCNN uses column and row local receptive fields to perform data abstraction, concatenating each direction's feature before connecting it to an FC layer. Experimental results demonstrate that the CRCNN maintains comparable accuracy while reducing the number of parameters and compared to prior work. Moreover, the CRCNN is employed for one-class anomaly detection, demonstrating its feasibility for various applications.

PMID:38457753 | DOI:10.1162/neco_a_01653

Biochemistry. 2024 Mar 8. doi: 10.1021/acs.biochem.3c00596. Online ahead of print.

ABSTRACT

Optimization of aptamers in length and chemistry is crucial for industrial applications. Here, we developed aptamers against the SARS-CoV-2 spike protein and achieved optimization with a deep-learning-based algorithm, RaptGen. We conducted a primer-less SELEX against the receptor binding domain (RBD) of the spike with an RNA/DNA hybrid library, and the resulting sequences were subjected to RaptGen analysis. Based on the sequence profiling by RaptGen, a short truncation aptamer of 26 nucleotides was obtained and further optimized by a chemical modification of relevant nucleotides. The resulting aptamer is bound to RBD not only of SARS-CoV-2 wildtype but also of its variants, SARS-CoV-1, and Middle East respiratory syndrome coronavirus (MERS-CoV). We concluded that the RaptGen-assisted discovery is efficient for developing optimized aptamers.

PMID:38457656 | DOI:10.1021/acs.biochem.3c00596

Medicine (Baltimore). 2024 Mar 8;103(10):e37528. doi: 10.1097/MD.0000000000037528.

ABSTRACT

BACKGROUND: Adolescents with hemiplegic cerebral palsy undergo conventional physical therapy (CPT) to improve static and dynamic balance, activities of daily living and cardiopulmonary function. To overcome this problem, we developed an innovative deep learning-based rehabilitation application (DRA) to provide a motivational and chaffed platform for such individuals. DRA evaluates the patients' functional abilities and diagnosis an appropriate therapeutic intervention like CPT.

METHODS: We compared the effects of DRA and CPT on 6-minute walking test (6 MWT), Borg rating of perceived exertion scale, Berg balance scale, functional ambulation category, and modified Barthel index in adolescents with hemiplegic cerebral palsy. A convenience sample of 30 adolescents with hemiplegic cerebral palsy was randomized into either the DRA or CPT group. DRA and CPT were administered to the participants, with each session lasting 30 minutes and apportioned thrice a week for a total of 4 weeks.

RESULTS: Analysis of variance was performed and the level of significance was set at P < .05. The analysis indicated that DRA showed therapeutic effects on 6 MWT, Berg balance scale, and modified Barthel index compared to CPT.

CONCLUSION: Our results provide evidence that DRA can improve cardiopulmonary function, balance, and activities of daily living more effectively than CPT in adolescents with hemiplegic cerebral palsy.

PMID:38457533 | DOI:10.1097/MD.0000000000037528

PLoS One. 2024 Mar 8;19(3):e0299448. doi: 10.1371/journal.pone.0299448. eCollection 2024.

ABSTRACT

BACKGROUND: Total marrow irradiation (TMI) and total marrow and lymphoid irradiation (TMLI) have the advantages. However, delineating target lesions according to TMI and TMLI plans is labor-intensive and time-consuming. In addition, although the delineation of target lesions between TMI and TMLI differs, the clinical distinction is not clear, and the lymph node (LN) area coverage during TMI remains uncertain. Accordingly, this study calculates the LN area coverage according to the TMI plan. Further, a deep learning-based model for delineating LN areas is trained and evaluated.

METHODS: Whole-body regional LN areas were manually contoured in patients treated according to a TMI plan. The dose coverage of the delineated LN areas in the TMI plan was estimated. To train the deep learning model for automatic segmentation, additional whole-body computed tomography data were obtained from other patients. The patients and data were divided into training/validation and test groups and models were developed using the "nnU-NET" framework. The trained models were evaluated using Dice similarity coefficient (DSC), precision, recall, and Hausdorff distance 95 (HD95). The time required to contour and trim predicted results manually using the deep learning model was measured and compared.

RESULTS: The dose coverage for LN areas by TMI plan had V100% (the percentage of volume receiving 100% of the prescribed dose), V95%, and V90% median values of 46.0%, 62.1%, and 73.5%, respectively. The lowest V100% values were identified in the inguinal (14.7%), external iliac (21.8%), and para-aortic (42.8%) LNs. The median values of DSC, precision, recall, and HD95 of the trained model were 0.79, 0.83, 0.76, and 2.63, respectively. The time for manual contouring and simply modified predicted contouring were statistically significantly different.

CONCLUSIONS: The dose coverage in the inguinal, external iliac, and para-aortic LN areas was suboptimal when treatment is administered according to the TMI plan. This research demonstrates that the automatic delineation of LN areas using deep learning can facilitate the implementation of TMLI.

PMID:38457432 | DOI:10.1371/journal.pone.0299448

PLOS Glob Public Health. 2024 Mar 8;4(3):e0002112. doi: 10.1371/journal.pgph.0002112. eCollection 2024.

ABSTRACT

The literature on mentorship approaches to capacity building in global health is limited. Likewise, there are few qualitative studies that describe mentorship in capacity building in global health from the perspective of the mentors and mentees. This qualitative study examined the perspectives and experiences of participants involved in a program of health capacity building in Timor-Leste that was based on a side-by-side, in-country mentorship approach. Semi-structured interviews were conducted with 23 participants (including Timorese and expatriate mentors, and local Timorese colleagues) from across a range of professional health disciplines, followed by a series of member checking workshops. Findings were reviewed using inductive thematic analysis. Participants were included in review and refinement of themes. Four major themes were identified: the importance of trust and connection within the mentoring relationship; the side-by-side nature of the relationship (akompaña); mentoring in the context of external environmental challenges; and the need for the mentoring relationship to be dynamic and evolving, and aligned to a shared vision and goals. The importance of accompaniment (akompaña) as a key element of the mentoring relationship requires further exploration and study. Many activities in global health capacity building remain focused on provision of training, supervision, and supportive supervision of competent task performance. Viewed through a decolonising lens, there is an imperative for global health actors to align with local priorities and goals, and work alongside individuals supporting them in their vision to become independent leaders of their professions. We propose that placing mentoring relationships at the centre of human resource capacity building programs encourages deep learning, and is more likely to lead to long term, meaningful and sustainable change.

PMID:38457415 | DOI:10.1371/journal.pgph.0002112

J Clin Gastroenterol. 2024 Mar 4. doi: 10.1097/MCG.0000000000001972. Online ahead of print.

ABSTRACT

BACKGROUND: Gastric structure recognition systems have become increasingly necessary for the accurate diagnosis of gastric lesions in capsule endoscopy. Deep learning, especially using transformer models, has shown great potential in the recognition of gastrointestinal (GI) images according to self-attention. This study aims to establish an identification model of capsule endoscopy gastric structures to improve the clinical applicability of deep learning to endoscopic image recognition.

METHODS: A total of 3343 wireless capsule endoscopy videos collected at Nanfang Hospital between 2011 and 2021 were used for unsupervised pretraining, while 2433 were for training and 118 were for validation. Fifteen upper GI structures were selected for quantifying the examination quality. We also conducted a comparison of the classification performance between the artificial intelligence model and endoscopists by the accuracy, sensitivity, specificity, and positive and negative predictive values.

RESULTS: The transformer-based AI model reached a relatively high level of diagnostic accuracy in gastric structure recognition. Regarding the performance of identifying 15 upper GI structures, the AI model achieved a macroaverage accuracy of 99.6% (95% CI: 99.5-99.7), a macroaverage sensitivity of 96.4% (95% CI: 95.3-97.5), and a macroaverage specificity of 99.8% (95% CI: 99.7-99.9) and achieved a high level of interobserver agreement with endoscopists.

CONCLUSIONS: The transformer-based AI model can accurately evaluate the gastric structure information of capsule endoscopy with the same performance as that of endoscopists, which will provide tremendous help for doctors in making a diagnosis from a large number of images and improve the efficiency of examination.

PMID:38457410 | DOI:10.1097/MCG.0000000000001972

Cancer Med. 2024 Feb;13(4):e7065. doi: 10.1002/cam4.7065.

ABSTRACT

INTRODUCTION: Near-infrared autofluorescence imaging (NIFI) can be used to identify parathyroid gland (PG) during surgery. The purpose of the study is to establish a new model, help surgeons better identify, and protect PGs.

METHODS: Five hundred and twenty three NIFI images were selected. The PGs were recorded by NIFI and marked with artificial intelligence (AI) model. The recognition rate for PGs was calculated. Analyze the differences between surgeons of different years of experience and AI recognition, and evaluate the diagnostic and therapeutic efficacy of AI model.

RESULTS: Our model achieved 83.5% precision and 57.8% recall in the internal validation set. The visual recognition rate of AI model was 85.2% and 82.4% on internal and external sets. The PG recognition rate of AI model is higher than that of junior surgeons (p < 0.05).

CONCLUSIONS: This AI model will help surgeons identify PGs, and develop their learning ability and self-confidence.

PMID:38457206 | DOI:10.1002/cam4.7065

J Xray Sci Technol. 2024 Mar 5. doi: 10.3233/XST-230251. Online ahead of print.

ABSTRACT

BACKGROUND: The error magnitude is closely related to patient-specific dosimetry and plays an important role in evaluating the delivery of the radiotherapy plan in QA. No previous study has investigated the feasibility of deep learning to predict error magnitude.

OBJECTIVE: The purpose of this study was to predict the error magnitude of different delivery error types in radiotherapy based on ResNet.

METHODS: A total of 34 chest cancer plans (172 fields) of intensity-modulated radiation therapy (IMRT) from Eclipse were selected, of which 30 plans (151 fields) were used for model training and validation, and 4 plans including 21 fields were used for external testing. The collimator misalignment (COLL), monitor unit variation (MU), random multi-leaf collimator shift (MLCR), and systematic MLC shift (MLCS) were introduced. These dose distributions of portal dose predictions for the original plans were defined as the reference dose distribution (RDD), while those for the error-introduced plans were defined as the error-introduced dose distribution (EDD). Different inputs were used in the ResNet for predicting the error magnitude.

RESULTS: In the test set, the accuracy of error type prediction based on the dose difference, gamma distribution, and RDD + EDD was 98.36%, 98.91%, and 100%, respectively; the root mean squared error (RMSE) was 1.45-1.54, 0.58-0.90, 0.32-0.36, and 0.15-0.24; the mean absolute error (MAE) was 1.06-1.18, 0.32-0.78, 0.25-0.27, and 0.11-0.18, respectively, for COLL, MU, MLCR and MLCS.

CONCLUSIONS: In this study, error magnitude prediction models with dose difference, gamma distribution, and RDD + EDD are established based on ResNet. The accurate prediction of the error magnitude under different error types can provide reference for error analysis in patient-specific QA.

PMID:38457139 | DOI:10.3233/XST-230251

Interdiscip Sci. 2024 Mar 8. doi: 10.1007/s12539-024-00609-y. Online ahead of print.

ABSTRACT

Accurately predicting compound-protein interactions (CPI) is a critical task in computer-aided drug design. In recent years, the exponential growth of compound activity and biomedical data has highlighted the need for efficient and interpretable prediction approaches. In this study, we propose GraphsformerCPI, an end-to-end deep learning framework that improves prediction performance and interpretability. GraphsformerCPI treats compounds and proteins as sequences of nodes with spatial structures, and leverages novel structure-enhanced self-attention mechanisms to integrate semantic and graph structural features within molecules for deep molecule representations. To capture the vital association between compound atoms and protein residues, we devise a dual-attention mechanism to effectively extract relational features through .cross-mapping. By extending the powerful learning capabilities of Transformers to spatial structures and extensively utilizing attention mechanisms, our model offers strong interpretability, a significant advantage over most black-box deep learning methods. To evaluate GraphsformerCPI, extensive experiments were conducted on benchmark datasets including human, C. elegans, Davis and KIBA datasets. We explored the impact of model depth and dropout rate on performance and compared our model against state-of-the-art baseline models. Our results demonstrate that GraphsformerCPI outperforms baseline models in classification datasets and achieves competitive performance in regression datasets. Specifically, on the human dataset, GraphsformerCPI achieves an average improvement of 1.6% in AUC, 0.5% in precision, and 5.3% in recall. On the KIBA dataset, the average improvement in Concordance index (CI) and mean squared error (MSE) is 3.3% and 7.2%, respectively. Molecular docking shows that our model provides novel insights into the intrinsic interactions and binding mechanisms. Our research holds practical significance in effectively predicting CPIs and binding affinities, identifying key atoms and residues, enhancing model interpretability.

PMID:38457109 | DOI:10.1007/s12539-024-00609-y

Eur J Nucl Med Mol Imaging. 2024 Mar 8. doi: 10.1007/s00259-024-06668-z. Online ahead of print.

ABSTRACT

PURPOSE: Multiple myeloma (MM) is a highly heterogeneous disease with wide variations in patient outcome. [18F]FDG PET/CT can provide prognostic information in MM, but it is hampered by issues regarding standardization of scan interpretation. Our group has recently demonstrated the feasibility of automated, volumetric assessment of bone marrow (BM) metabolic activity on PET/CT using a novel artificial intelligence (AI)-based tool. Accordingly, the aim of the current study is to investigate the prognostic role of whole-body calculations of BM metabolism in patients with newly diagnosed MM using this AI tool.

MATERIALS AND METHODS: Forty-four, previously untreated MM patients underwent whole-body [18F]FDG PET/CT. Automated PET/CT image segmentation and volumetric quantification of BM metabolism were based on an initial CT-based segmentation of the skeleton, its transfer to the standardized uptake value (SUV) PET images, subsequent application of different SUV thresholds, and refinement of the resulting regions using postprocessing. In the present analysis, ten different uptake thresholds (AI approaches), based on reference organs or absolute SUV values, were applied for definition of pathological tracer uptake and subsequent calculation of the whole-body metabolic tumor volume (MTV) and total lesion glycolysis (TLG). Correlation analysis was performed between the automated PET values and histopathological results of the BM as well as patients' progression-free survival (PFS) and overall survival (OS). Receiver operating characteristic (ROC) curve analysis was used to investigate the discrimination performance of MTV and TLG for prediction of 2-year PFS. The prognostic performance of the new Italian Myeloma criteria for PET Use (IMPeTUs) was also investigated.

RESULTS: Median follow-up [95% CI] of the patient cohort was 110 months [105-123 months]. AI-based BM segmentation and calculation of MTV and TLG were feasible in all patients. A significant, positive, moderate correlation was observed between the automated quantitative whole-body PET/CT parameters, MTV and TLG, and BM plasma cell infiltration for all ten [18F]FDG uptake thresholds. With regard to PFS, univariable analysis for both MTV and TLG predicted patient outcome reasonably well for all AI approaches. Adjusting for cytogenetic abnormalities and BM plasma cell infiltration rate, multivariable analysis also showed prognostic significance for high MTV, which defined pathological [18F]FDG uptake in the BM via the liver. In terms of OS, univariable and multivariable analysis showed that whole-body MTV, again mainly using liver uptake as reference, was significantly associated with shorter survival. In line with these findings, ROC curve analysis showed that MTV and TLG, assessed using liver-based cut-offs, could predict 2-year PFS rates. The application of IMPeTUs showed that the number of focal hypermetabolic BM lesions and extramedullary disease had an adverse effect on PFS.

CONCLUSIONS: The AI-based, whole-body calculations of BM metabolism via the parameters MTV and TLG not only correlate with the degree of BM plasma cell infiltration, but also predict patient survival in MM. In particular, the parameter MTV, using the liver uptake as reference for BM segmentation, provides solid prognostic information for disease progression. In addition to highlighting the prognostic significance of automated, global volumetric estimation of metabolic tumor burden, these data open up new perspectives towards solving the complex problem of interpreting PET scans in MM with a simple, fast, and robust method that is not affected by operator-dependent interventions.

PMID:38456971 | DOI:10.1007/s00259-024-06668-z

J Med Syst. 2024 Mar 8;48(1):30. doi: 10.1007/s10916-024-02040-8.

ABSTRACT

Although magnetic resonance imaging (MRI) data of patients with multiple myeloma (MM) are used to predict prognosis, few reports have applied artificial intelligence (AI) techniques for this purpose. We aimed to analyze whole-body diffusion-weighted MRI data using three-dimensional (3D) convolutional neural networks (CNNs) and Gradient-weighted Class Activation Mapping (Grad-CAM), an explainable AI, to predict prognosis and explore the factors involved in prediction. We retrospectively analyzed the MRI data of a total of 142 patients with MM obtained from two medical centers. We defined the occurrence of progressive disease after MRI evaluation within 12 months as a poor prognosis and constructed a 3D CNN-based deep learning model to predict prognosis. Images from 111 cases were used as the training and internal validation data; images from 31 cases were used as the external validation data. Internal validation of the AI model with stratified 5-fold cross-validation resulted in a significant difference in progression-free survival (PFS) between good and poor prognostic cases (2-year PFS, 91.2% versus [vs.] 61.1%, P = 0.0002). The AI model clearly stratified good and poor prognostic cases in the external validation cohort (2-year PFS, 92.9% vs. 55.6%, P = 0.004), with an area under the receiver operating characteristic curve of 0.804. According to Grad-CAM, the MRI signals of the spleen and bones of the vertebrae and pelvis contributed to prognosis prediction. This study is the first to show that image analysis of whole-body MRI using a 3D CNN without any other clinical data is effective in predicting the prognosis of patients with MM.

PMID:38456950 | DOI:10.1007/s10916-024-02040-8

Lab Chip. 2024 Mar 8. doi: 10.1039/d3lc00694h. Online ahead of print.

ABSTRACT

Metastatic tumors have poor prognoses for progression-free and overall survival for all cancer patients. Rare circulating tumor cells (CTCs) and rarer circulating tumor cell clusters (CTCCs) are potential biomarkers of metastatic growth, with CTCCs representing an increased risk factor for metastasis. Current detection platforms are optimized for ex vivo detection of CTCs only. Microfluidic chips and size exclusion methods have been proposed for CTCC detection; however, they lack in vivo utility and real-time monitoring capability. Confocal backscatter and fluorescence flow cytometry (BSFC) has been used for label-free detection of CTCCs in whole blood based on machine learning (ML) enabled peak classification. Here, we expand to a deep-learning (DL)-based, peak detection and classification model to detect CTCCs in whole blood data. We demonstrate that DL-based BSFC has a low false alarm rate of 0.78 events per min with a high Pearson correlation coefficient of 0.943 between detected events and expected events. DL-based BSFC of whole blood maintains a detection purity of 72% and a sensitivity of 35.3% for both homotypic and heterotypic CTCCs starting at a minimum size of two cells. We also demonstrate through artificial spiking studies that DL-based BSFC is sensitive to changes in the number of CTCCs present in the samples and does not add variability in detection beyond the expected variability from Poisson statistics. The performance established by DL-based BSFC motivates its use for in vivo detection of CTCCs. Using transfer learning, we additionally validate DL-based BSFC on blood samples from different species and cancer cell types. Further developments of label-free BSFC to enhance throughput could lead to critical applications in the clinical detection of CTCCs and ex vivo isolation of CTCC from whole blood with minimal disruption and processing steps.

PMID:38456773 | DOI:10.1039/d3lc00694h

J Agromedicine. 2024 Mar 8:1-3. doi: 10.1080/1059924X.2024.2326556. Online ahead of print.

ABSTRACT

The agriculture industry lacks novel techniques for analyzing risks facing its workers. Although injuries are common in this field, existing datasets and tools are insufficient for risk assessment and mitigation for two primary reasons: they provide neither immediate nor long-term risk mitigation advice, and they do not account for hazards which fluctuate daily. The purpose of Demeter is to collect safety data about hazards on farms and produce risk analysis and mitigation reports. This application uses a combination of formula-based risk calculations and state-of-the-art graph neural networks (GNNs) to perform risk analysis and reduction. The formula-based risk calculations had a mean absolute error (MAE) of 0.2110, and the GNN had an accuracy of 94.9%, a precision of 0.3521, and a recall of 0.8333. Demeter has the potential to reduce the number of injuries and fatalities among agriculture workers by alerting them to risks present in their daily workflow and suggesting safety precautions.

PMID:38456661 | DOI:10.1080/1059924X.2024.2326556

Adv Healthc Mater. 2024 Mar 8:e2400241. doi: 10.1002/adhm.202400241. Online ahead of print.

ABSTRACT

Pancreatic cancer is a highly malignant and metastatic cancer. Pancreatic cancer can lead to liver metastases, gallbladder metastases, and duodenum metastases. The identification of pancreatic cancer cells is essential for the diagnosis of metastatic cancer and exploration of carcinoma in situ. Organelles play an important role in maintaining the function of cells, the various cells show significant difference in organelle microenvironment. Herein, six probes were synthesized for targeting mitochondria, lysosomes, cell membranes, endoplasmic reticulum, Golgi apparatus, and lipid droplets. The six fluorescent probes formed OT-SA (an organelles-targeted sensor array) to image pancreatic metastatic cancer cells and cell spheroids. The homology of metastatic cancer cells brings the challenge for identification of these cells. The residual network (ResNet) model has been proven to automatically extract and select image features, which can figure out a subtle difference among the similar samples. Hence, OT-SA was developed to identify pancreatic metastasis cells and cell spheroids combination with ResNet analysis. The identification accuracy for the pancreatic metastasis cells (> 99%) and pancreatic metastasis cell spheroids (> 99%) in test set was successfully achieved respectively. The organelles-targeting sensor array provided a method for the identification of pancreatic cancer metastasis in cells and cell spheroids. This article is protected by copyright. All rights reserved.

PMID:38456344 | DOI:10.1002/adhm.202400241

Front Neurol. 2024 Feb 21;15:1326591. doi: 10.3389/fneur.2024.1326591. eCollection 2024.

ABSTRACT

BACKGROUND: This study focused on minimizing the costs and toxic effects associated with unnecessary chemotherapy. We sought to optimize the adjuvant therapy strategy, choosing between radiotherapy (RT) and chemoradiotherapy (CRT), for patients based on their specific characteristics. This selection process utilized an innovative deep learning method.

METHODS: We trained six machine learning (ML) models to advise on the most suitable treatment for glioblastoma (GBM) patients. To assess the protective efficacy of these ML models, we employed various metrics: hazards ratio (HR), inverse probability treatment weighting (IPTW)-adjusted HR (HRa), the difference in restricted mean survival time (dRMST), and the number needed to treat (NNT).

RESULTS: The Balanced Individual Treatment Effect for Survival data (BITES) model emerged as the most effective, demonstrating significant protective benefits (HR: 0.53, 95% CI, 0.48-0.60; IPTW-adjusted HR: 0.65, 95% CI, 0.55-0.78; dRMST: 7.92, 95% CI, 7.81-8.15; NNT: 1.67, 95% CI, 1.24-2.41). Patients whose treatment aligned with BITES recommendations exhibited notably better survival rates compared to those who received different treatments, both before and after IPTW adjustment. In the CRT-recommended group, a significant survival advantage was observed when choosing CRT over RT (p < 0.001). However, this was not the case in the RT-recommended group (p = 0.06). Males, older patients, and those whose tumor invasion is confined to the ventricular system were more frequently advised to undergo RT.

CONCLUSION: Our study suggests that BITES can effectively identify GBM patients likely to benefit from CRT. These ML models show promise in transforming the complex heterogeneity of real-world clinical practice into precise, personalized treatment recommendations.

PMID:38456152 | PMC:PMC10919147 | DOI:10.3389/fneur.2024.1326591

Lancet Reg Health West Pac. 2023 Dec 13;43:100979. doi: 10.1016/j.lanwpc.2023.100979. eCollection 2024 Feb.

ABSTRACT

BACKGROUND: Psychopathological and behavioral problems in adolescence are highly comorbid, making their developmental trajectories complex and unclear partly due to technical limitations. We aimed to classify these trajectories using deep learning and identify predictors of cluster membership.

METHODS: We conducted a population-based cohort study on 3171 adolescents from three Tokyo municipalities, with 2344 pairs of adolescents and caregivers participating at all four timepoints (ages 10, 12, 14, and 16) from 2012 to 2021. Adolescent psychopathological and behavioral problems were assessed by using self-report questionnaires. Both adolescents and caregivers assessed depression/anxiety and psychotic-like experiences. Caregivers assessed obsession/compulsion, dissociation, sociality problem, hyperactivity/inattention, conduct problem, somatic symptom, and withdrawal. Adolescents assessed desire for slimness, self-harm, and suicidal ideation. These trajectories were clustered with variational deep embedding with recurrence, and predictors were explored using multinomial logistic regression.

FINDINGS: Five clusters were identified: unaffected (60.5%), minimal problems; internalizing (16.2%), persistent or worsening internalizing problems; discrepant (9.9%), subjective problems overlooked by caregivers; externalizing (9.6%), persistent externalizing problems; and severe (3.9%), chronic severe problems across symptoms. Stronger autistic traits and experience of bullying victimization commonly predicted the four "affected" clusters. The discrepant cluster, showing the highest risks for self-harm and suicidal ideation, was predicted by avoiding help-seeking for depression. The severe cluster predictors included maternal smoking during pregnancy, not bullying others, caregiver's psychological distress, and adolescent's dissatisfaction with family.

INTERPRETATION: Approximately 40% of adolescents were classified as "affected" clusters. Proactive societal attention is warranted toward adolescents in the discrepant cluster whose suicidality is overlooked and who have difficulty seeking help.

FUNDING: Japan Ministry of Health, Labor and Welfare, Japan Agency for Medical Research and Development, and Japan Science and Technology Agency.

PMID:38456092 | PMC:PMC10920037 | DOI:10.1016/j.lanwpc.2023.100979

Front Bioeng Biotechnol. 2024 Feb 22;12:1355735. doi: 10.3389/fbioe.2024.1355735. eCollection 2024.

ABSTRACT

Rapid and accurate muscle segmentation is essential for the diagnosis and monitoring of many musculoskeletal diseases. As gold standard, manual annotation suffers from intensive labor and high inter-operator reproducibility errors. In this study, deep learning (DL) based automatic muscle segmentation from MR scans is investigated for post-menopausal women, who normally experience a decline in muscle volume. The performance of four Deep Learning (DL) models was evaluated: U-Net and UNet++ and two modified U-Net networks, which combined feature fusion and attention mechanisms (Feature-Fusion-UNet, FFU, and Attention-Feature-Fusion-UNet, AFFU). The models were tested for automatic segmentation of 16-lower limb muscles from MRI scans of two cohorts of post-menopausal women (11 subjects in PMW-1, 8 subjects in PMW-2; from two different studies so considered independent datasets) and 10 obese post-menopausal women (PMW-OB). Furthermore, a novel data augmentation approach is proposed to enlarge the training dataset. The results were assessed and compared by using the Dice similarity coefficient (DSC), relative volume error (RVE), and Hausdorff distance (HD). The best performance among all four DL models was achieved by AFFU (PMW-1: DSC 0.828 ± 0.079, 1-RVE 0.859 ± 0.122, HD 29.9 mm ± 26.5 mm; PMW-2: DSC 0.833 ± 0.065, 1-RVE 0.873 ± 0.105, HD 25.9 mm ± 27.9 mm; PMW-OB: DSC 0.862 ± 0.048, 1-RVE 0.919 ± 0.076, HD 34.8 mm ± 46.8 mm). Furthermore, the augmentation of data significantly improved the DSC scores of U-Net and AFFU for all 16 tested muscles (between 0.23% and 2.17% (DSC), 1.6%-1.93% (1-RVE), and 9.6%-19.8% (HD) improvement). These findings highlight the feasibility of utilizing DL models for automatic segmentation of muscles in post-menopausal women and indicate that the proposed augmentation method can enhance the performance of models trained on small datasets.

PMID:38456001 | PMC:PMC10919285 | DOI:10.3389/fbioe.2024.1355735

Front Physiol. 2024 Feb 22;15:1304829. doi: 10.3389/fphys.2024.1304829. eCollection 2024.

ABSTRACT

Introduction: Precise classification has an important role in treatment of pressure injury (PI), while current machine-learning or deeplearning based methods of PI classification remain low accuracy. Methods: In this study, we developed a deeplearning based weighted feature fusion architecture for fine-grained classification, which combines a top-down and bottom-up pathway to fuse high-level semantic information and low-level detail representation. We validated it in our established database that consist of 1,519 images from multi-center clinical cohorts. ResNeXt was set as the backbone network. Results: We increased the accuracy of stage 3 PI from 60.3% to 76.2% by adding weighted feature pyramid network (wFPN). The accuracy for stage 1, 2, 4 PI were 0.870, 0.788, and 0.845 respectively. We found the overall accuracy, precision, recall, and F1-score of our network were 0.815, 0.808, 0.816, and 0.811 respectively. The area under the receiver operating characteristic curve was 0.940. Conclusions: Compared with current reported study, our network significantly increased the overall accuracy from 75% to 81.5% and showed great performance in predicting each stage. Upon further validation, our study will pave the path to the clinical application of our network in PI management.

PMID:38455845 | PMC:PMC10917912 | DOI:10.3389/fphys.2024.1304829

JAMIA Open. 2024 Feb 21;7(1):ooae021. doi: 10.1093/jamiaopen/ooae021. eCollection 2024 Apr.

ABSTRACT

OBJECTIVE: To automate scientific claim verification using PubMed abstracts.

MATERIALS AND METHODS: We developed CliVER, an end-to-end scientific Claim VERification system that leverages retrieval-augmented techniques to automatically retrieve relevant clinical trial abstracts, extract pertinent sentences, and use the PICO framework to support or refute a scientific claim. We also created an ensemble of three state-of-the-art deep learning models to classify rationale of support, refute, and neutral. We then constructed CoVERt, a new COVID VERification dataset comprising 15 PICO-encoded drug claims accompanied by 96 manually selected and labeled clinical trial abstracts that either support or refute each claim. We used CoVERt and SciFact (a public scientific claim verification dataset) to assess CliVER's performance in predicting labels. Finally, we compared CliVER to clinicians in the verification of 19 claims from 6 disease domains, using 189 648 PubMed abstracts extracted from January 2010 to October 2021.

RESULTS: In the evaluation of label prediction accuracy on CoVERt, CliVER achieved a notable F1 score of 0.92, highlighting the efficacy of the retrieval-augmented models. The ensemble model outperforms each individual state-of-the-art model by an absolute increase from 3% to 11% in the F1 score. Moreover, when compared with four clinicians, CliVER achieved a precision of 79.0% for abstract retrieval, 67.4% for sentence selection, and 63.2% for label prediction, respectively.

CONCLUSION: CliVER demonstrates its early potential to automate scientific claim verification using retrieval-augmented strategies to harness the wealth of clinical trial abstracts in PubMed. Future studies are warranted to further test its clinical utility.

PMID:38455840 | PMC:PMC10919922 | DOI:10.1093/jamiaopen/ooae021