AIMC Topic: Tomography, X-Ray Computed
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Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.
Cancer imaging : the official publication of the International Cancer Imaging Society
May 9, 2024
BACKGROUND: This study systematically compares the impact of innovative deep learning image reconstruction (DLIR, TrueFidelity) to conventionally used iterative reconstruction (IR) on nodule volumetry and subjective image quality (IQ) at highly reduc...
Weakly-Supervised Segmentation-Based Quantitative Characterization of Pulmonary Cavity Lesions in CT Scans.
IEEE journal of translational engineering in health and medicine
May 9, 2024
OBJECTIVE: Pulmonary cavity lesion is one of the commonly seen lesions in lung caused by a variety of malignant and non-malignant diseases. Diagnosis of a cavity lesion is commonly based on accurate recognition of the typical morphological characteri...
Natural language processing augments comorbidity documentation in neurosurgical inpatient admissions.
PloS one
May 9, 2024
OBJECTIVE: To establish whether or not a natural language processing technique could identify two common inpatient neurosurgical comorbidities using only text reports of inpatient head imaging.
Deep learning imaging phenotype can classify metabolic syndrome and is predictive of cardiometabolic disorders.
Journal of translational medicine
May 8, 2024
BACKGROUND: Cardiometabolic disorders pose significant health risks globally. Metabolic syndrome, characterized by a cluster of potentially reversible metabolic abnormalities, is a known risk factor for these disorders. Early detection and interventi...
Development and external validation of a multimodal integrated feature neural network (MIFNN) for the diagnosis of malignancy in small pulmonary nodules (≤10 mm).
Biomedical physics & engineering express
May 8, 2024
. Current lung cancer screening protocols primarily evaluate pulmonary nodules, yet often neglect the malignancy risk associated with small nodules (≤10 mm). This study endeavors to optimize the management of pulmonary nodules in this population by d...
Characterizing Osteophyte Formation in Knee Osteoarthritis: Application of Machine Learning Quantification of a Computerized Tomography Cohort: Implications for Treatment.
The Journal of arthroplasty
May 7, 2024
BACKGROUND: Osteophytes are commonly used to diagnose and guide knee osteoarthritis (OA) treatment, but their causes are unclear. Although they are not typically the focus of knee arthroplasty surgeons, they can predict case difficulty and length. Fu...
Applications of Artificial Intelligence in Acute Abdominal Imaging.
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
May 7, 2024
Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of ...
Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.
Journal of surgical oncology
May 7, 2024
BACKGROUND AND OBJECTIVES: Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on pre...
Novel dosimetric validation of a commercial CT scanner based deep learning automated contour solution for prostate radiotherapy.
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
May 7, 2024
PURPOSE: OAR delineation accuracy influences: (i) a patient's optimised dose distribution (PD), (ii) the reported doses (RD) presented at approval, which represent plan quality. This study utilised a novel dosimetric validation methodology, comprehen...
Investigation of a potential upstream harmonization based on image appearance matching to improve radiomics features robustness: a phantom study.
Biomedical physics & engineering express
May 7, 2024
. Radiomics is a promising valuable analysis tool consisting in extracting quantitative information from medical images. However, the extracted radiomics features are too sensitive to variations in used image acquisition and reconstruction parameters...
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