AIMC Topic: Multimodal Imaging

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Integrating biometric and multimodal imaging data for early prediction of myopia onset.

Scientific reports
Myopia is a growing global health concern, and early detection and intervention are crucial for preventing its onset and progression. This study aims to predict myopia onset one year in advance by integrating biometric measurements with multimodal im...

Multimodal imaging deep learning model for predicting extraprostatic extension in prostate cancer using MpMRI and 18 F-PSMA-PET/CT.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVE: This study aimed to construct a multimodal imaging deep learning (DL) model integrating mpMRI and F-PSMA-PET/CT for the prediction of extraprostatic extension (EPE) in prostate cancer, and to assess its effectiveness in enhancing the diagn...

An explainable predictive machine learning model for axillary lymph node metastasis in breast cancer based on multimodal data: A retrospective single-center study.

Journal of translational medicine
OBJECTIVE: To develop explainable machine learning models that integrate multimodal imaging and pathological biomarkers to predict axillary lymph node metastasis (ALNM) in breast cancer patients and assess their clinical utility.

Multimodal radiomics in glioma: predicting recurrence in the peritumoural brain zone using integrated MRI.

BMC medical imaging
BACKGROUND: Gliomas exhibit a high recurrence rate, particularly in the peritumoural brain zone after surgery. This study aims to develop and validate a radiomics-based model using preoperative fluid-attenuated inversion recovery (FLAIR) and T1-weigh...

The evolving role of multimodal imaging, artificial intelligence and radiomics in the radiologic assessment of immune related adverse events.

Clinical imaging
Immunotherapy, in particular checkpoint blockade, has revolutionized the treatment of many advanced cancers. Imaging plays a critical role in assessing both treatment response and the development of immune toxicities. Both conventional imaging and mo...

Multi-modal classification of retinal disease based on convolutional neural network.

Biomedical physics & engineering express
Retinal diseases such as age-related macular degeneration and diabetic retinopathy will lead to irreversible blindness without timely diagnosis and treatment. Optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) imag...

Multimodal neuroimaging unveils basal forebrain-limbic system circuit dysregulation in cognitive impairment with depression: a pathway to early diagnosis and intervention.

The journal of prevention of Alzheimer's disease
BACKGROUND: Alzheimer's disease (AD) frequently co-occurs with depressive symptoms, exacerbating both cognitive decline and clinical complexity, yet the neural substrates linking this co-occurrence remain poorly understood. We aimed to investigate th...

An end-to-end interpretable machine-learning-based framework for early-stage diagnosis of gallbladder cancer using multi-modality medical data.

BMC cancer
BACKGROUND: The accurate early-stage diagnosis of gallbladder cancer (GBC) is regarded as one of the major challenges in the field of oncology. However, few studies have focused on the comprehensive classification of GBC based on multiple modalities....

Multimodal Deep Learning Model Based on Ultrasound and Cytological Images Predicts Risk Stratification of cN0 Papillary Thyroid Carcinoma.

Academic radiology
BACKGROUND: Accurately assessing the risk stratification of cN0 papillary thyroid carcinoma (PTC) preoperatively aids in making treatment decisions. We integrated preoperative ultrasound and cytological images of patients to develop and validate a mu...