AIMC Topic: Female

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Development of a machine learning model and nomogram to predict seizures in children with COVID-19: a two-center study.

Journal of tropical pediatrics
OBJECTIVE: This study aimed to use machine learning to evaluate the risk factors of seizures and develop a model and nomogram to predict seizures in children with coronavirus disease 2019 (COVID-19).

BlastAssist: a deep learning pipeline to measure interpretable features of human embryos.

Human reproduction (Oxford, England)
STUDY QUESTION: Can the BlastAssist deep learning pipeline perform comparably to or outperform human experts and embryologists at measuring interpretable, clinically relevant features of human embryos in IVF?

Trends in the Prevalence of Common Retinal and Optic Nerve Diseases in China: An Artificial Intelligence Based National Screening.

Translational vision science & technology
PURPOSE: Retinal and optic nerve diseases have become the primary cause of irreversible vision loss and blindness. However, there is still a lack of thorough evaluation regarding their prevalence in China.

A prediction model for reactivation of Langerhans cell histiocytosis based on machine-learning algorithms.

European journal of dermatology : EJD
Langerhans cell histiocytosis (LCH) is a rare inflammatory myeloid neoplasm characterized by the clonal proliferation of myeloid progenitor cells. The reactivation rate of LCH exceeds 30%. However, an effective prediction model to predict reactivatio...

[Analysis of clinical treatment of acute respiratory distress syndrome assisted by artificial intelligence].

Zhonghua wei zhong bing ji jiu yi xue
OBJECTIVE: To evaluate the clinical practice of intensive care unit (ICU) physicians at Hebei General Hospital in identifying patients meeting the diagnostic criteria for acute respiratory distress syndrome (ARDS) and the current status of invasive m...

[Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].

Zhonghua wei zhong bing ji jiu yi xue
OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.

Advancements in oligometastatic breast cancer: a comprehensive review of current strategies and the role of artificial intelligence.

JPMA. The Journal of the Pakistan Medical Association
In the dynamic landscape of Breast Cancer (BC), Oligo- Metastatic Breast Cancer (OMBC) presents unique challenges and opportunities. This comprehensive review delves into current strategies for addressing OMBC, covering locoregional and site-specific...

Artificial intelligence-powered optimization of KI-67 assessment in breast cancer: enhancing precision and workflow efficiency. a literature review.

JPMA. The Journal of the Pakistan Medical Association
Breast Cancer (BC) has evolved from traditional morphological analysis to molecular profiling, identifying new subtypes. Ki-67, a prognostic biomarker, helps classify subtypes and guide chemotherapy decisions. This review explores how artificial inte...

The discerning influence of dynamic contrast-enhanced MRI in anticipating molecular subtypes of breast cancer through the artistry of artificial intelligence - a narrative review.

JPMA. The Journal of the Pakistan Medical Association
Radio genomics is an exciting new area that uses diagnostic imaging to discover genetic features of diseases. In this review, we carefully examined existing literature to evaluate the role of artificial intelligence (AI) and machine learning (ML) on ...

Transforming breast cancer care: harnessing the power of artificial intelligence and imaging for predicting pathological complete response. a narrative review.

JPMA. The Journal of the Pakistan Medical Association
This narrative review explores the transformative potential of Artificial Intelligence (AI) and advanced imaging techniques in predicting Pathological Complete Response (pCR) in Breast Cancer (BC) patients undergoing Neo-Adjuvant Chemotherapy (NACT)....