Peter Brotchie
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View article: Visual Perception and Fixation Patterns in an Individual with Ventral Simultanagnosia, Integrative Agnosia and Bilateral Visual Field Loss
Visual Perception and Fixation Patterns in an Individual with Ventral Simultanagnosia, Integrative Agnosia and Bilateral Visual Field Loss Open
Background/Objectives: As high-acuity vision is limited to a very small visual angle, examination of a scene requires multiple fixations. Simultanagnosia, a disorder wherein elements of a scene can be perceived correctly but cannot be inte…
View article: Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer
Comparison of AI-integrated pathways with human-AI interaction in population mammographic screening for breast cancer Open
Artificial intelligence (AI) readers of mammograms compare favourably to individual radiologists in detecting breast cancer. However, AI readers cannot perform at the level of multi-reader systems used by screening programs in countries su…
View article: Using neural networks to autonomously assess adequacy in intraoperative cholangiograms
Using neural networks to autonomously assess adequacy in intraoperative cholangiograms Open
View article: AutoCumulus: an Automated Mammographic Density Measure Created Using Artificial Intelligence
AutoCumulus: an Automated Mammographic Density Measure Created Using Artificial Intelligence Open
Background Mammographic (or breast) density is an established risk factor for breast cancer. There are a variety of approaches to measurement including quantitative, semi-automated and automated approaches. We present a new automated measu…
View article: Braix Risk Score: An Automated Mammogram-Based Biomarker for Breast Cancer Created by Applying Artificial Intelligence
Braix Risk Score: An Automated Mammogram-Based Biomarker for Breast Cancer Created by Applying Artificial Intelligence Open
View article: Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy
Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy Open
Objectives Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interp…
View article: Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review
Machine Learning Augmented Interpretation of Chest X-rays: A Systematic Review Open
Limitations of the chest X-ray (CXR) have resulted in attempts to create machine learning systems to assist clinicians and improve interpretation accuracy. An understanding of the capabilities and limitations of modern machine learning sys…
View article: ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets
ADMANI: Annotated Digital Mammograms and Associated Non-Image Datasets Open
Supplemental material is available for this article. Keywords: Mammography, Screening, Convolutional Neural Network (CNN) Published under a CC BY 4.0 license. See also the commentary by Cadrin-Chênevert in this issue.
View article: Comparison of AI-integrated pathways with human-AI interaction for population mammographic screening
Comparison of AI-integrated pathways with human-AI interaction for population mammographic screening Open
Artificial intelligence (AI) holds promise for improving breast cancer screening, but many challenges remain in implementing AI tools in clinical screening services. AI readers compare favourably against individual human radiologists in de…
View article: Effects of a comprehensive brain computed tomography deep-learning model on radiologist detection accuracy: a multireader, multicase study
Effects of a comprehensive brain computed tomography deep-learning model on radiologist detection accuracy: a multireader, multicase study Open
Background: Non-contrast computed tomography of the brain (NCCTB) is commonly used in clinical practice to detect intracranial pathology but is subject to interpretation errors. Machine learning is capable of augmenting clinical decision m…
View article: Charting the potential of brain computed tomography deep learning systems
Charting the potential of brain computed tomography deep learning systems Open
Brain computed tomography (CTB) scans are widely used to evaluate intracranial pathology. The implementation and adoption of CTB has led to clinical improvements. However, interpretation errors occur and may have substantial morbidity and …
View article: Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography
Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography Open
Objectives To evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an…
View article: Distal Medium Vessel Occlusions Can Be Accurately and Rapidly Detected Using <i>Tmax</i> Maps
Distal Medium Vessel Occlusions Can Be Accurately and Rapidly Detected Using <i>Tmax</i> Maps Open
Background and Purpose: Distal medium vessel occlusions (DMVOs) are increasingly considered for endovascular thrombectomy but are difficult to detect on computed tomography angiography (CTA). We aimed to determine whether time-to-maximum o…
View article: Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study
Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study Open
Annalise.ai.
View article: Evaluation of deep learning‐based artificial intelligence techniques for breast cancer detection on mammograms: Results from a retrospective study using a BreastScreen Victoria dataset
Evaluation of deep learning‐based artificial intelligence techniques for breast cancer detection on mammograms: Results from a retrospective study using a BreastScreen Victoria dataset Open
Introduction This study aims to evaluate deep learning (DL)‐based artificial intelligence (AI) techniques for detecting the presence of breast cancer on a digital mammogram image. Methods We evaluated several DL‐based AI techniques that em…
View article: Incidental detection of prostate cancer with computed tomography scans
Incidental detection of prostate cancer with computed tomography scans Open
View article: Motor functions of monkey globus pallidus
Motor functions of monkey globus pallidus Open
This thesis was scanned from the print manuscript for digital preservation and is copyright the author. Researchers can access this thesis by asking their local university, institution or public library to make a request on their behalf. M…
View article: Time-to-Maximum of the Tissue Residue Function Improves Diagnostic Performance for Detecting Distal Vessel Occlusions on CT Angiography
Time-to-Maximum of the Tissue Residue Function Improves Diagnostic Performance for Detecting Distal Vessel Occlusions on CT Angiography Open
All assessed metrics of diagnostic performance for detecting distal arterial occlusions improved with the use of time-to-maximum of the tissue residue function maps, encouraging their use to aid in interpretation of CTA by both experienced…
View article: Sequences show rapid motor transfer and spatial translation in the oculomotor system
Sequences show rapid motor transfer and spatial translation in the oculomotor system Open