AIMC Topic: Ultrasonography
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GBCHV an advanced deep learning anatomy aware model for accurate classification of gallbladder cancer utilizing ultrasound images.
Scientific reports
Feb 28, 2025
This study introduces a novel deep learning approach aimed at accurately classifying Gallbladder Cancer (GBC) into benign, malignant, and normal categories using ultrasound images from the challenging GBC USG (GBCU) dataset. The proposed methodology ...
Deep Learning for Ultrasonographic Assessment of Temporomandibular Joint Morphology.
Tomography (Ann Arbor, Mich.)
Feb 27, 2025
BACKGROUND: Temporomandibular joint (TMJ) disorders are a significant cause of orofacial pain. Artificial intelligence (AI) has been successfully applied to other imaging modalities but remains underexplored in ultrasonographic evaluations of TMJ.
Predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients based on ultrasound longitudinal temporal depth network fusion model.
Breast cancer research : BCR
Feb 27, 2025
OBJECTIVE: The aim of this study was to develop and validate a deep learning radiomics (DLR) model based on longitudinal ultrasound data and clinical features to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NAC) in breas...
Development of an artificial intelligence-based multimodal diagnostic system for early detection of biliary atresia.
BMC medicine
Feb 27, 2025
BACKGROUND: Early diagnosis of biliary atresia (BA) is crucial for improving patient outcomes, yet remains a significant global challenge. This challenge may be ameliorated through the application of artificial intelligence (AI). Despite the promise ...
Machine learning-driven ultrasound radiomics for assessing axillary lymph node burden in breast cancer.
Frontiers in endocrinology
Feb 27, 2025
OBJECTIVE: This study explores the value of combining intratumoral and peritumoral radiomics features from ultrasound imaging with clinical characteristics to assess axillary lymph node burden in breast cancer patients.
Non-invasive classification of non-neoplastic and neoplastic gallbladder polyps based on clinical imaging and ultrasound radiomics features: An interpretable machine learning model.
European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology
Feb 25, 2025
BACKGROUND: Gallbladder (GB) adenomas, precancerous lesions for gallbladder carcinoma (GBC), lack reliable non-invasive tools for preoperative differentiation of neoplastic polyps from cholesterol polyps. This study aimed to evaluate an interpretable...
TRUSWorthy: toward clinically applicable deep learning for confident detection of prostate cancer in micro-ultrasound.
International journal of computer assisted radiology and surgery
Feb 20, 2025
PURPOSE: While deep learning methods have shown great promise in improving the effectiveness of prostate cancer (PCa) diagnosis by detecting suspicious lesions from trans-rectal ultrasound (TRUS), they must overcome multiple simultaneous challenges. ...
Artificial intelligence and different image modalities in uveal melanoma diagnosis and prognosis: A narrative review.
Photodiagnosis and photodynamic therapy
Feb 20, 2025
BACKGROUND: The most widespread primary intraocular tumor in adults is called uveal melanoma (UM), if detected early enough, it can be curable. Various methods are available to treat UM, but the most commonly used and effective approach is plaque rad...
SegFormer3D: Improving the Robustness of Deep Learning Model-Based Image Segmentation in Ultrasound Volumes of the Pediatric Hip.
Ultrasound in medicine & biology
Feb 19, 2025
Developmental dysplasia of the hip (DDH) is a painful orthopedic malformation diagnosed at birth in 1-3% of all newborns. Left untreated, DDH can lead to significant morbidity including long-term disability. Currently the condition is clinically diag...
Artificial intelligence in the diagnosis of uveal melanoma: advances and applications.
Experimental biology and medicine (Maywood, N.J.)
Feb 19, 2025
Advancements in machine learning and deep learning have the potential to revolutionize the diagnosis of melanocytic choroidal tumors, including uveal melanoma, a potentially life-threatening eye cancer. Traditional machine learning methods rely heavi...
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