AIMC Topic: Female

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YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Both mass detection and segmentation in digital mammograms play a crucial role in early breast cancer detection and treatment. Furthermore, clinical experience has shown that they are the upstream tasks of pathological class...

Deep Learning to Predict Mortality After Cardiothoracic Surgery Using Preoperative Chest Radiographs.

The Annals of thoracic surgery
BACKGROUND: The Society of Thoracic Surgeons Predicted Risk of Mortality (STS-PROM) estimates mortality risk only for certain common procedures (eg, coronary artery bypass or valve surgery) and is cumbersome, requiring greater thanĀ 60 inputs. We hypo...

Follow-Up of Robotic Mitral Valve Repair: A Single Tertiary Institution Experience in China.

Computational and mathematical methods in medicine
Surgical treatment of mitral valve diseases has become minimally invasive. This study analyzed the follow-up results of patients after mitral valve repairs (MVRep) using the da Vinci robot. The clinical data of patients who underwent minimally invasi...

Deep learning-based multimodal image analysis for cervical cancer detection.

Methods (San Diego, Calif.)
Cervical cancer is the fourth most common cancer in women, and its precise detection plays a critical role in disease treatment and prognosis prediction. Fluorodeoxyglucose positron emission tomography and computed tomography, i.e., FDG-PET/CT and PE...

A Deep Learning Model Based on MRI and Clinical Factors Facilitates Noninvasive Evaluation of KRAS Mutation in Rectal Cancer.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Recent studies showed the potential of MRI-based deep learning (DL) for assessing treatment response in rectal cancer, but the role of MRI-based DL in evaluating Kirsten rat sarcoma viral oncogene homologue (KRAS) mutation remains unclear...

Artificial intelligence for prediction of treatment outcomes in breast cancer: Systematic review of design, reporting standards, and bias.

Cancer treatment reviews
BACKGROUND: Artificial intelligence (AI) has the potential to personalize treatment strategies for patients with cancer. However, current methodological weaknesses could limit clinical impact. We identified common limitations and suggested potential ...

Combining multiparametric MRI features-based transfer learning and clinical parameters: application of machine learning for the differentiation of uterine sarcomas from atypical leiomyomas.

European radiology
OBJECTIVES: To explore the feasibility and effectiveness of machine learning (ML) based on multiparametric magnetic resonance imaging (mp-MRI) features extracted from transfer learning combined with clinical parameters to differentiate uterine sarcom...

Fast T2-weighted liver MRI: Image quality and solid focal lesions conspicuity using a deep learning accelerated single breath-hold HASTE fat-suppressed sequence.

Diagnostic and interventional imaging
PURPOSE: Acceleration of MRI acquisitions and especially of T2-weighted sequences is essential to reduce the duration of MRI examinations but also kinetic artifacts in liver imaging. The purpose of this study was to compare the acquisition time and t...

PFP-LHCINCA: Pyramidal Fixed-Size Patch-Based Feature Extraction and Chi-Square Iterative Neighborhood Component Analysis for Automated Fetal Sex Classification on Ultrasound Images.

Contrast media & molecular imaging
OBJECTIVES: Fetal sex determination with ultrasound (US) examination is indicated in pregnancies at risk of X-linked genetic disorders or ambiguous genitalia. However, misdiagnoses often arise due to operator inexperience and technical difficulties w...

Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study.

Radiology
Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate t...