AIMC Topic: Aged

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Individualized Treatment Effect Prediction with Machine Learning - Salient Considerations.

NEJM evidence
BACKGROUND: Machine learning-based approaches that seek to accomplish individualized treatment effect prediction have gained traction; however, some salient challenges lack wider recognition.

Minimally invasive cytoreductive radical prostatectomy, exploring the safety and feasibility of a single-port or multi-port robotic platform.

BMC urology
BACKGROUND: Consolidative resection or cytoreductive radical prostatectomy (CRP) may benefit men with non-organ confined prostate cancer. We report the safety, feasibility, and outcomes of robot-assisted laparoscopic CRP using a single-port (SP) or m...

Artificial intelligence-based video monitoring of movement disorders in the elderly: a review on current and future landscapes.

Singapore medical journal
Due to global ageing, the burden of chronic movement and neurological disorders (Parkinson's disease and essential tremor) is rapidly increasing. Current diagnosis and monitoring of these disorders rely largely on face-to-face assessments utilising c...

Prospective validation of a machine learning model for applicator and hybrid interstitial needle selection in high-dose-rate (HDR) cervical brachytherapy.

Brachytherapy
PURPOSE: To Demonstrate the clinical validation of a machine learning (ML) model for applicator and interstitial needle prediction in gynecologic brachytherapy through a prospective clinical study in a single institution.

Combined brain topological metrics with machine learning to distinguish essential tremor and tremor-dominant Parkinson's disease.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND: Essential tremor (ET) and Parkinson's disease (PD) are the two most prevalent movement disorders, sharing several overlapping tremor clinical features. Although growing evidence pointed out that changes in similar brain network nodes are ...

A Deep Learning-Based Assessment Pipeline for Intraepithelial and Stromal Tumor-Infiltrating Lymphocytes in High-Grade Serous Ovarian Carcinoma.

The American journal of pathology
Tumor-infiltrating lymphocytes (TILs) are associated with improved survival in patients with epithelial ovarian cancer. However, TIL evaluation has not been used in routine clinical practice because of reproducibility issues. The current study develo...

Serum Fusion Transcripts to Assess the Risk of Hepatocellular Carcinoma and the Impact of Cancer Treatment through Machine Learning.

The American journal of pathology
Hepatocellular carcinoma (HCC) is one of the most fatal malignancies. Early diagnosis of HCC is crucial in reducing the risk for mortality. This study analyzed a panel of nine fusion transcripts in serum samples from 61 patients with HCC and 75 patie...

Machine learning-based analysis of Ga-PSMA-11 PET/CT images for estimation of prostate tumor grade.

Physical and engineering sciences in medicine
Early diagnosis of prostate cancer, the most common malignancy in men, can improve patient outcomes. Since the tissue sampling procedures are invasive and sometimes inconclusive, an alternative image-based method can prevent possible complications an...

From pixels to prognosis: Imaging biomarkers for discrimination and outcome prediction of pulmonary embolism : Original Research Article.

Emergency radiology
PURPOSE: Recent advancements in medical imaging have transformed diagnostic assessments, offering exciting possibilities for extracting biomarker-based information. This study aims to investigate the capabilities of a machine learning classifier that...