AIMC Topic: Case-Control Studies

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Machine Learning and Electroencephalogram Signal based Diagnosis of Dipression.

Neuroscience letters
Depression is a psychological condition which hampers day to day activity (Thinking, Feeling or Action). The early detection of this illness will help to save many lives because it is now recognized as a global problem which could even lead to suicid...

Effects of tracer position on screw placement technique in robot-assisted posterior spine surgery: a case-control study.

BMC musculoskeletal disorders
INTRODUCTION: Robot-assisted spine surgery is increasingly used in clinical work, and the installation of tracers as a key step in robotic surgery has rarely been studied.

Graphic Intelligent Diagnosis of Hypoxic-Ischemic Encephalopathy Using MRI-Based Deep Learning Model.

Neonatology
INTRODUCTION: Heterogeneous MRI manifestations restrict the efficiency and consistency of neuroradiologists in diagnosing hypoxic-ischemic encephalopathy (HIE) due to complex injury patterns. This study aimed to develop and validate an intelligent HI...

Higher depression risks in medium- than in high-density urban form across Denmark.

Science advances
Urban areas are associated with higher depression risks than rural areas. However, less is known about how different types of urban environments relate to depression risk. Here, we use satellite imagery and machine learning to quantify three-dimensio...

Salivary and serum levels of soluble E-cadherin in patients with gastrointestinal cancers: A comparative study.

Journal of cancer research and therapeutics
AIM: According to the literature, high levels of salivary soluble E-cadherin may be lined to advanced stage and poor prognosis in cancers. This research aimed at comparing salivary and serum levels of soluble E-cadherin in cases with esophageal, gast...

Machine Learning Model for Assessment of Risk Factors and Postoperative Day for Superficial vs Deep/Organ-Space Surgical Site Infections.

Surgical innovation
Deep and organ space surgical site infections (SSI) require more intensive treatment, may result in more severe clinical disease and may have different risk factors when compared to superficial SSIs. Machine learning (ML) algorithms provide the oppo...

Deep learning neural network image analysis of immunohistochemical protein expression reveals a significantly reduced expression of biglycan in breast cancer.

PloS one
New breast cancer biomarkers have been sought for better tumor characterization and treatment. Among these putative markers, there is Biglycan (BGN). BGN is a class I small leucine-rich proteoglycan family of proteins characterized by a protein core ...

Developing a machine learning model to detect diagnostic uncertainty in clinical documentation.

Journal of hospital medicine
BACKGROUND AND OBJECTIVE: Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to ...

Equivalent radiation exposure with robotic total hip replacement using a novel, fluoroscopic-guided (CT-free) system: case-control study versus manual technique.

Journal of robotic surgery
Accurate and precise positioning of the acetabular cup remains a prevalent challenge in total hip arthroplasty (THA). Robotic assistance for THA has increased over the past decade due to the potential to improve the accuracy of implant placement. How...

Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models.

International journal of molecular sciences
Recent deep learning algorithms have further improved risk classification capabilities. However, an appropriate feature selection method is required to overcome dimensionality issues in population-based genetic studies. In this Korean case-control st...