Publications by authors named "J Tetreault"
Autism spectrum disorder (ASD) is characterized by impairments in social affective engagement. The present study uses a mild social stressor task to add to inconclusive past literature concerning differences in affective expressivity between autistic young adults and non-autistic individuals from the general population (GP). Young adults (mean age = 21.
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- A study evaluated an AI model's ability to detect prostate cancer in scans done at different institutions, focusing on biparametric MRI (bpMRI) scans from both an external and an in-house setup.
- This research included 201 male patients and showed that the AI detected a greater percentage of lesions on in-house scans compared to external ones (56.0% vs. 39.7% for intraprostatic lesions and 79% vs. 61% for clinically significant prostate cancer).
- Factors that improved the AI's detection rates included higher PI-RADS scores, larger lesion sizes, and better quality of diffusion-weighted MRI images.
Background Multiparametric MRI (mpMRI) improves prostate cancer (PCa) detection compared with systematic biopsy, but its interpretation is prone to interreader variation, which results in performance inconsistency. Artificial intelligence (AI) models can assist in mpMRI interpretation, but large training data sets and extensive model testing are required. Purpose To evaluate a biparametric MRI AI algorithm for intraprostatic lesion detection and segmentation and to compare its performance with radiologist readings and biopsy results.
View Article and Find Full Text PDFRationale And Objectives: Extraprostatic extension (EPE) is well established as a significant predictor of prostate cancer aggression and recurrence. Accurate EPE assessment prior to radical prostatectomy can impact surgical approach. We aimed to utilize a deep learning-based AI workflow for automated EPE grading from prostate T2W MRI, ADC map, and High B DWI.
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- This study assesses the effectiveness of three AI algorithms for segmenting prostate regions in MRI scans of patients with complex medical backgrounds and varied anatomical features.
- The researchers analyzed data from 683 MRI scans, ensuring that they included criteria such as previous treatments and different scanner qualities, and compared the AI’s segmentation against expert radiologist assessments.
- Results showed that deep learning models significantly outperformed other methods, especially in cases with smaller prostate volumes and better image quality, highlighting the challenges presented by variances in anatomy and scan conditions.