End-to-end artificial intelligence platform for the management of large vessel occlusions: A preliminary study.

OBJECTIVES: In this study, we developed a deep learning pipeline that detects large vessel occlusion (LVO) and predicts functional outcome based on computed tomography angiography (CTA) images to improve the management of the LVO patients. METHODS: A series identifier picked out 8650 LVO-protocoled studies from 2015 to 2019 at Rhode Island Hospital with an identified thin axial series that served as the data pool. Data were annotated into 2 classes: 1021 LVOs and 7629 normal. The Inception-V1 I3D architecture was applied for LVO detection. For outcome prediction, 323 patients undergoing thrombectomy were selected. A 3D convolution neural network (CNN) was used for outcome prediction (30-day mRS) with CTA volumes and embedded pre-treatment variables as inputs. RESULT: For LVO-detection model, CTAs from 8,650 patients (median age 68 years, interquartile range (IQR): 58-81; 3934 females) were analyzed. The cross-validated AUC for LVO vs. not was 0.74 (95% CI: 0.72-0.75). For the mRS classification model, CTAs from 323 patients (median age 75 years, IQR: 63-84; 164 females) were analyzed. The algorithm achieved a test AUC of 0.82 (95% CI: 0.79-0.84), sensitivity of 89%, and specificity 66%. The two models were then integrated with hospital infrastructure where CTA was collected in real-time and processed by the model. If LVO was detected, interventionists were notified and provided with predicted clinical outcome information. CONCLUSION: 3D CNNs based on CTA were effective in selecting LVO and predicting LVO mechanical thrombectomy short-term prognosis. End-to-end AI platform allows users to receive immediate prognosis prediction and facilitates clinical workflow.

Multi-modality Regional Alignment Network for Covid X-Ray Survival Prediction and Report Generation.

In response to the worldwide COVID-19 pandemic, advanced automated technologies have emerged as valuable tools to aid healthcare professionals in managing an increased workload by improving radiology report generation and prognostic analysis. This study proposes a Multi-modality Regional Alignment Network (MRANet), an explainable model for radiology report generation and survival prediction that focuses on high-risk regions. By learning spatial correlation in the detector, MRANet visually grounds region-specific descriptions, providing robust anatomical regions with a completion strategy. The visual features of each region are embedded using a novel survival attention mechanism, offering spatially and risk-aware features for sentence encoding while maintaining global coherence across tasks. A cross-domain LLMs-Alignment is employed to enhance the image-to-text transfer process, resulting in sentences rich with clinical detail and improved explainability for radiologists. Multi-center experiments validate the overall performance and each module's composition within the model, encouraging further advancements in radiology report generation research emphasizing clinical interpretation and trustworthiness in AI models applied to medical studies.

De-Biased Disentanglement Learning for Pulmonary Embolism Survival Prediction on Multimodal Data.

Health disparities among marginalized populations with lower socioeconomic status significantly impact the fairness and effectiveness of healthcare delivery. The increasing integration of artificial intelligence (AI) into healthcare presents an opportunity to address these inequalities, provided that AI models are free from bias. This paper aims to address the bias challenges by population disparities within healthcare systems, existing in the presentation of and development of algorithms, leading to inequitable medical implementation for conditions such as pulmonary embolism (PE) prognosis. In this study, we explore the diverse bias in healthcare systems, which highlights the demand for a holistic framework to reducing bias by complementary aggregation. By leveraging de-biasing deep survival prediction models, we propose a framework that disentangles identifiable information from images, text reports, and clinical variables to mitigate potential biases within multimodal datasets. Our study offers several advantages over traditional clinical-based survival prediction methods, including richer survival-related characteristics and bias-complementary predicted results. By improving the robustness of survival analysis through this framework, we aim to benefit patients, clinicians, and researchers by enhancing fairness and accuracy in healthcare AI systems.

MRI-Based Prediction of Clinical Improvement Following Ventricular Shunt Placement for Normal Pressure Hydrocephalus (NPH): Development and Evaluation of an Integrated Multi-Sequence Machine Learning Algorithm.

BACKGROUND AND PURPOSE: Symptoms of normal pressure hydrocephalus (NPH) are sometimes refractory to shunt placement, with limited ability to predict improvement for individual patients. We evaluated an MRI-based artificial intelligence method to predict post-shunt NPH symptom improvement. MATERIALS AND METHODS: NPH patients who underwent magnetic resonance imaging (MRI) prior to shunt placement at a single center (2014-2021) were identified. Twelve-month post-shunt improvement in modified Rankin Scale (mRS), incontinence, gait, and cognition were retrospectively abstracted from clinical documentation. 3D deep residual neural networks were built on skull stripped T2-weighted and fluid attenuated inversion recovery (FLAIR) images. Predictions based on both sequences were fused by additional network layers. Patients from 2014-2019 were used for parameter optimization, while those from 2020-2021 were used for testing. Models were validated on an external validation dataset from a second institution (n=33). RESULTS: Of 249 patients, n=201 and n=185 were included in the T2-based and FLAIR-based models according to imaging availability. The combination of T2-weighted and FLAIR sequences offered the best performance in mRS and gait improvement predictions relative to models trained on imaging acquired using only one sequence, with AUROC values of 0.7395 [0.5765-0.9024] for mRS and 0.8816 [0.8030-0.9602] for gait. For urinary incontinence and cognition, combined model performances on predicting outcomes were similar to FLAIR-only performance, with AUROC values of 0.7874 [0.6845-0.8903] and 0.7230 [0.5600-0.8859]. CONCLUSIONS: Application of a combined algorithm using both T2-weighted and FLAIR sequences offered the best image-based prediction of post-shunt symptom improvement, particularly for gait and overall function in terms of mRS. ABBREVIATIONS: NPH = normal pressure hydrocephalus; iNPH = idiopathic NPH; sNPH = secondary NPH; AI = artificial intelligence; ML = machine learning; CSF = cerebrospinal fluid; AUROC = area under the receiver operating characteristic; FLAIR = fluid attenuated inversion recovery; BMI = body mass index; CCI = Charlson Comorbidity Index; SD = standard deviation; IQR = interquartile range.

Prediction of lung malignancy progression and survival with machine learning based on pre-treatment FDG-PET/CT.

BACKGROUND: Pre-treatment FDG-PET/CT scans were analyzed with machine learning to predict progression of lung malignancies and overall survival (OS). METHODS: A retrospective review across three institutions identified patients with a pre-procedure FDG-PET/CT and an associated malignancy diagnosis. Lesions were manually and automatically segmented, and convolutional neural networks (CNNs) were trained using FDG-PET/CT inputs to predict malignancy progression. Performance was evaluated using area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. Image features were extracted from CNNs and by radiomics feature extraction, and random survival forests (RSF) were constructed to predict OS. Concordance index (C-index) and integrated brier score (IBS) were used to evaluate OS prediction. FINDINGS: 1168 nodules (n=965 patients) were identified. 792 nodules had progression and 376 were progression-free. The most common malignancies were adenocarcinoma (n=740) and squamous cell carcinoma (n=179). For progression risk, the PET+CT ensemble model with manual segmentation (accuracy=0.790, AUC=0.876) performed similarly to the CT only (accuracy=0.723, AUC=0.888) and better compared to the PET only (accuracy=0.664, AUC=0.669) models. For OS prediction with deep learning features, the PET+CT+clinical RSF ensemble model (C-index=0.737) performed similarly to the CT only (C-index=0.730) and better than the PET only (C-index=0.595), and clinical only (C-index=0.595) models. RSF models constructed with radiomics features had comparable performance to those with CNN features. INTERPRETATION: CNNs trained using pre-treatment FDG-PET/CT and extracted performed well in predicting lung malignancy progression and OS. OS prediction performance with CNN features was comparable to a radiomics approach. The prognostic models could inform treatment options and improve patient care. FUNDING: NIH NHLBI training grant (5T35HL094308-12, John Sollee).

Evaluation and comparison of accurate automated spinal curvature estimation algorithms with spinal anterior-posterior X-Ray images: The AASCE2019 challenge.

Scoliosis is a common medical condition, which occurs most often during the growth spurt just before puberty. Untreated Scoliosis may cause long-term sequelae. Therefore, accurate automated quantitative estimation of spinal curvature is an important task for the clinical evaluation and treatment planning of Scoliosis. A couple of attempts have been made for automated Cobb angle estimation on single-view x-rays. It is very challenging to achieve a highly accurate automated estimation of Cobb angles because it is difficult to utilize x-rays efficiently. With the idea of developing methods for accurate automated spinal curvature estimation, AASCE2019 challenge provides spinal anterior-posterior x-ray images with manual labels for training and testing the participating methods. We review eight top-ranked methods from 12 teams. Experimental results show that overall the best performing method achieved a symmetric mean absolute percentage (SMAPE) of 21.71%. Limitations and possible future directions are also described in the paper. We hope the dataset in AASCE2019 and this paper could provide insights into quantitative measurement of the spine.