Study DesignRetrospective study.ObjectiveTo develop a machine learning model for predicting axial symptoms (AS) after unilateral laminoplasty by integrating C2 spinous process muscle radiomics features and cervical sagittal parameters.MethodsIn this retrospective study of 96 cervical myelopathy patients (30 with AS, 66 without) who underwent unilateral laminoplasty between 2018-2022, we extracted radiomics features from preoperative MRI of C2 spinous muscles using PyRadiomics. Clinical data including C2-C7 Cobb angle, cervical sagittal vertical axis (cSVA), T1 slope (T1S) and C2 muscle fat infiltration are collected for clinical model construction. After LASSO regression feature selection, we constructed six machine learning models (SVM, KNN, Random Forest, ExtraTrees, XGBoost, and LightGBM) and evaluated their performance using ROC curves and AUC.ResultsThe AS group demonstrated significantly lower preoperative C2-C7 Cobb angles (12.80° ± 7.49° vs 18.02° ± 8.59°, <i>P</i> = .006), higher cSVA (3.01 cm ± 0.87 vs 2.46 ± 1.19 cm, <i>P</i> = .026), T1S (26.68° ± 5.12° vs 23.66° ± 7.58°, <i>P</i> = .025) and higher C2 muscle fat infiltration (23.73 ± 7.78 vs 20.62 ± 6.93 <i>P</i> = .026). Key radiomics features included local binary pattern texture features and wavelet transform characteristics. The combined model integrating radiomics and clinical parameters achieved the best performance with test AUC of 0.881, sensitivity of 0.833, and specificity of 0.786.ConclusionThe machine learning model based on C2 spinous process muscle radiomics features and clinical parameters (C2-C7 Cobb angle, cSVA, T1S and C2 muscle infiltration) effectively predicts AS occurrence after unilateral laminoplasty, providing clinicians with a valuable tool for preoperative risk assessment and personalized treatment planning.

Data binary encoding has proven to be a versatile tool for optimizing data processing and memory efficiency in various machine learning applications. This includes deep barcoding, generating barcodes from deep learning feature extraction for image retrieval of similar cases among millions of indexed images. Despite the recent advancement in barcode generation methods, converting high-dimensional feature vectors (e.g., deep features) to compact and discriminative binary barcodes is still an urgent necessity and remains an unresolved problem. Difference-based binarization of features is one of the most efficient binarization methods, transforming continuous feature vectors into binary sequences and capturing trend information. However, the performance of this method is highly dependent on the ordering of the input features, leading to a significant combinatorial challenge. This research addresses this problem by optimizing feature sequences based on retrieval performance metrics. Our approach identifies optimal feature orderings, leading to substantial improvements in retrieval effectiveness compared to arbitrary or default orderings. We assess the performance of the proposed approach in various medical and non-medical image retrieval tasks. This evaluation includes medical images from The Cancer Genome Atlas (TCGA), a comprehensive publicly available dataset, as well as COVID-19 Chest X-rays dataset. In addition, we evaluate the proposed approach on non-medical benchmark image datasets, such as CIFAR-10, CIFAR-100, and Fashion-MNIST. Our findings demonstrate the importance of optimizing binary barcode representation to significantly enhance accuracy for fast image retrieval across a wide range of applications, highlighting the applicability and potential of barcodes in various domains.

Spinal radiology reports and physician-completed questionnaires serve as crucial resources for medical decision-making for patients experiencing low back and neck pain. However, due to the time-consuming nature of this process, individuals with severe conditions may experience a deterioration in their health before receiving professional care. In this work, we propose an ensemble framework built on top of pre-trained BERT-based models which can classify patients on their need for surgery given their different data modalities including radiology reports and questionnaires. Our results demonstrate that our approach exceeds previous studies, effectively integrating information from multiple data modalities and serving as a valuable tool to assist physicians in decision making.

This paper reviews the current research on Alzheimer's disease and the use of deep learning, particularly 3D-convolutional neural networks (3D-CNN), in analyzing brain images. It presents a predictive model based on MRI and clinical data from the ADNI dataset, showing that deep learning can improve diagnosis accuracy and sensitivity. We also discuss potential applications in biomarker discovery, disease progression prediction, and personalised treatment planning, highlighting the ability to identify sensitive features for early diagnosis.

This study aims to enhance the accuracy of fetal ultrasound image classification using convolutional neural networks, specifically EfficientNet. The research focuses on data collection, preprocessing, model training, and evaluation at different pregnancy stages: early, midterm, and newborn. EfficientNet showed the best performance, particularly in the newborn stage, demonstrating deep learning's potential to improve classification performance and support clinical workflows.

Routine duplex ultrasound surveillance is recommended after femoral-popliteal and femoral-tibial-pedal vein bypass grafts at various post-operative intervals. Currently, there is no systematic method for bypass graft surveillance using a set of peak systolic velocities (PSVs) collected during these exams. This research aims to explore the use of recurrent neural networks to predict the next set of PSVs, which can then indicate occlusion status. Recurrent neural network models were developed to predict occlusion and stenosis based on one to three prior sets of PSVs, with a sequence-to-sequence model utilized to forecast future PSVs within the stent graft and nearby arteries. The study employed 5-fold cross-validation for model performance comparison, revealing that the BiGRU model outperformed BiLSTM when two or more sets of PSVs were included, demonstrating that increasing duplex ultrasound exams improve prediction accuracy and reduces error rates. This work establishes a basis for integrating comprehensive clinical data, including demographics, comorbidities, symptoms, and other risk factors, with PSVs to enhance lower extremity bypass graft surveillance predictions.

The U.S. Food and Drug Administration (FDA) plays an important role in ensuring safety and effectiveness of AI/ML-enabled devices through its regulatory processes. In recent years, there has been an increase in the number of these devices cleared by FDA. This study analyzes 104 FDA-approved ML-enabled medical devices from May 2021 to April 2023, extending previous research to provide a contemporary perspective on this evolving landscape. We examined clinical task, device task, device input and output, ML method and level of autonomy. Most approvals (n = 103) were via the 510(k) premarket notification pathway, indicating substantial equivalence to existing devices. Devices predominantly supported diagnostic tasks (n = 81). The majority of devices used imaging data (n = 99), with CT and MRI being the most common modalities. Device autonomy levels were distributed as follows: 52% assistive (requiring users to confirm or approve AI provided information or decision), 27% autonomous information, and 21% autonomous decision. The prevalence of assistive devices indicates a cautious approach to integrating ML into clinical decision-making, favoring support rather than replacement of human judgment.

This study proposes RRG-LLM, a model designed to enhance RRG by effectively learning medical domain with minimal computational resources. Initially, LLM is finetuned by LoRA, enabling efficient adaptation to the medical domain. Subsequently, only the linear projection layer that project the image into text is finetuned to extract important information from the radiology image and project it onto the text dimension. Proposed model demonstrated notable improvements in report generation. The performance of ROUGE-L was improved by 0.096 (51.7%) and METEOR by 0.046 (42.85%) compared to the baseline model.

The diagnosis of craniosynostosis, a condition involving the premature fusion of cranial sutures in infants, is essential for ensuring timely treatment and optimal surgical outcomes. Current diagnostic approaches often require CT scans, which expose children to significant radiation risks. To address this, we present a novel deep learning-based model utilizing multi-view X-ray images for craniosynostosis detection. The proposed model integrates advanced multi-view fusion (MVF) and cross-attention mechanisms, effectively combining features from three X-ray views (AP, lateral right, lateral left) and patient metadata (age, sex). By leveraging these techniques, the model captures comprehensive semantic and structural information for high diagnostic accuracy while minimizing radiation exposure. Tested on a dataset of 882 X-ray images from 294 pediatric patients, the model achieved an AUROC of 0.975, an F1-score of 0.882, a sensitivity of 0.878, and a specificity of 0.937. Grad-CAM visualizations further validated its ability to localize disease-relevant regions using only classification annotations. The model demonstrates the potential to revolutionize pediatric care by providing a safer, cost-effective alternative to CT scans.

This study aimed to develop and evaluate an artificial intelligence model to predict 28-day mortality of pneumonia patients at the time of disposition from emergency department (ED). A multicenter retrospective study was conducted on data from pneumonia patients who visited the ED of a tertiary academic hospital for 8 months and from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We combined chest X-ray information, clinical data, and CURB-65 score to develop three models with the CURB-65 score as a baseline. A total of 2,874 ED visits were analyzed. The RSF model using CXR, clinical data and CURB-65 achieved a C-index of 0.872 in test set, significantly outperforming the CURB-65 score. This study developed a prediction model in pneumonia patients' prognosis, highlighting the potential for supporting clinical decision making in ED through multi-modal clinical information.