AIMC Topic: Female

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Comparison of surgical outcomes between robot-assisted laparoscopic hysterectomy and conventional total laparoscopic hysterectomy in gynecologic benign disease: a single-center cohort study.

Journal of robotic surgery
We compared the surgical outcomes of robot-assisted laparoscopic hysterectomy (RAH) and total laparoscopic hysterectomy (TLH). This single-center cohort study compared 139 RAH cases from January, 2017 to September, 2021 and 291 TLH cases between Janu...

Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies.

Journal of cancer research and clinical oncology
BACKGROUND: Breast cancer is a major public health concern, and early diagnosis and classification are critical for effective treatment. Machine learning and deep learning techniques have shown great promise in the classification and diagnosis of bre...

Deep Learning Approaches with Digital Mammography for Evaluating Breast Cancer Risk, a Narrative Review.

Tomography (Ann Arbor, Mich.)
Breast cancer remains the leading cause of cancer-related deaths in women worldwide. Current screening regimens and clinical breast cancer risk assessment models use risk factors such as demographics and patient history to guide policy and assess ris...

A deep-learning-based clinical risk stratification for overall survival in adolescent and young adult women with breast cancer.

Journal of cancer research and clinical oncology
OBJECTIVE: The objective of this study is to construct a novel clinical risk stratification for overall survival (OS) prediction in adolescent and young adult (AYA) women with breast cancer.

External validation of a model for selecting day 3 embryos for transfer based upon deep learning and time-lapse imaging.

Reproductive biomedicine online
RESEARCH QUESTION: Could objective embryo assessment using iDAScore Version 2.0 perform as well as conventional morphological assessment?

Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.

Sensors (Basel, Switzerland)
Coronavirus disease 2019 (COVID-19) has seen a crucial outburst for both females and males worldwide. Automatic lung infection detection from medical imaging modalities provides high potential for increasing the treatment for patients to tackle COVID...

Patient groups in Rheumatoid arthritis identified by deep learning respond differently to biologic or targeted synthetic DMARDs.

PLoS computational biology
Cycling of biologic or targeted synthetic disease modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA) patients due to non-response is a problem preventing and delaying disease control. We aimed to assess and validate treatment res...

[Interest of iDAScore (intelligent Data Analysis Score) for embryo selection in routine IVF laboratory practice: Results of a preliminary study].

Gynecologie, obstetrique, fertilite & senologie
INTRODUCTION: Embryo selection is a major challenge in ART, especially since the generalization of single embryo transfer, and its optimization could lead to the improvement of clinical results in IVF. Recently, several Artificial Intelligence (AI) m...

Deep Learning Model Based on Dual-Modal Ultrasound and Molecular Data for Predicting Response to Neoadjuvant Chemotherapy in Breast Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To carry out radiomics analysis/deep convolutional neural network (CNN) based on B-mode ultrasound (BUS) and shear wave elastography (SWE) to predict response to neoadjuvant chemotherapy (NAC) in breast cancer patients.