AIMC Topic: Adult

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Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography.

La Radiologia medica
PURPOSE: To determine how automation bias (inclination of humans to overly trust-automated decision-making systems) can affect radiologists when interpreting AI-detected cerebral aneurysm findings in time-of-flight magnetic resonance angiography (TOF...

Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study.

AJR. American journal of roentgenology
Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images. The purpose of this study was to develop an artificial intelligence (AI) model to detect CRC on ro...

Efficacy and effectiveness of therapist-guided internet versus face-to-face cognitive behavioural therapy for depression via counterfactual inference using naturalistic registers and machine learning in Finland: a retrospective cohort study.

The lancet. Psychiatry
BACKGROUND: According to meta-analyses of randomised controlled trials (RCTs), therapist-guided internet-delivered cognitive behavioural therapy (iCBT) is as effective a treatment for depression as traditional face-to-face CBT (fCBT), despite its sub...

Comparative analysis of machine learning models for predicting pathological complete response to neoadjuvant chemotherapy in breast cancer: An MRI radiomics approach.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: The aim of this work is to compare different machine learning models for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer using radiomics features from dynamic contrast-enhanced magnetic reso...

Classification of fundus autofluorescence images based on macular function in retinitis pigmentosa using convolutional neural networks.

Japanese journal of ophthalmology
PURPOSE: To determine whether convolutional neural networks (CNN) can classify the severity of central vision loss using fundus autofluorescence (FAF) images and color fundus images of retinitis pigmentosa (RP), and to evaluate the utility of those i...

A metabolic fingerprint of ovarian cancer: a novel diagnostic strategy employing plasma EV-based metabolomics and machine learning algorithms.

Journal of ovarian research
Ovarian cancer (OC) is the third most common malignant tumor of women and is accompanied by an alteration of systemic metabolism. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for OC diagnosis. ...

Prediction of adverse pregnancy outcomes using machine learning techniques: evidence from analysis of electronic medical records data in Rwanda.

BMC medical informatics and decision making
BACKGROUND: Despite substantial progress in maternal and neonatal health, Rwanda's mortality rates remain high, necessitating innovative approaches to meet health related Sustainable Development Goals (SDGs). By leveraging data collected from Electro...

The large language model diagnoses tuberculous pleural effusion in pleural effusion patients through clinical feature landscapes.

Respiratory research
BACKGROUND: Tuberculous pleural effusion (TPE) is a challenging extrapulmonary manifestation of tuberculosis, with traditional diagnostic methods often involving invasive surgery and being time-consuming. While various machine learning and statistica...

Deep learning-based prediction of possibility for immediate implant placement using panoramic radiography.

Scientific reports
In this study, we investigated whether deep learning-based prediction of immediate implant placement is possible. Panoramic radiographs of 201 patients with 874 teeth (Group 1: 440 teeth difficult to place implant immediately after extraction, Group ...

Learning based lower limb joint kinematic estimation using open source IMU data.

Scientific reports
This study introduces a deep learning framework for estimating lower-limb joint kinematics using inertial measurement units (IMUs). While deep learning methods avoid sensor drift, extensive calibration, and complex setup procedures, they require subs...