Rory Martin

Rory Martin

Adelaide, South Australia, Australia
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About

I am a determined, outgoing and driven individual. I am passionate in cloud automation…

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Experience

  • Elders Graphic

    Elders

    South Australia, Australia

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    Adelaide, Australia

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    South Australia, Australia

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    Level 1/209 War Memorial Dr, North Adelaide SA 5006

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    Adelaide, South Australia, Australia

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    Mawson Lakes, South Australia

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    Adelaide, Australia

Education

Licenses & Certifications

Volunteer Experience

Publications

  • Diagnostic Accuracy of Artificial Intelligence (AI) tDetect Early Neoplasia in Barrett's Esophagus: A Non-comparative Systematic Review and Meta-Analysis

    Frontiers in Medicine

    Background and Aims: Artificial Intelligence (AI) is rapidly evolving in gastrointestinal (GI) endoscopy. We undertook a systematic review and meta-analysis to assess the performance of AI at detecting early Barrett's neoplasia.

    Methods: We searched Medline, EMBASE and Cochrane Central Register of controlled trials database from inception to the 28th Jan 2022 to identify studies on the detection of early Barrett's neoplasia using AI. Study quality was assessed using Quality Assessment of…

    Background and Aims: Artificial Intelligence (AI) is rapidly evolving in gastrointestinal (GI) endoscopy. We undertook a systematic review and meta-analysis to assess the performance of AI at detecting early Barrett's neoplasia.

    Methods: We searched Medline, EMBASE and Cochrane Central Register of controlled trials database from inception to the 28th Jan 2022 to identify studies on the detection of early Barrett's neoplasia using AI. Study quality was assessed using Quality Assessment of Diagnostic Accuracy Studies – 2 (QUADAS-2). A random-effects model was used to calculate pooled sensitivity, specificity, and diagnostics odds ratio (DOR). Forest plots and a summary of the receiving operating characteristics (SROC) curves displayed the outcomes. Heterogeneity was determined by I2, Tau2 statistics and p-value. The funnel plots and Deek's test were used to assess publication bias.

    Results: Twelve studies comprising of 1,361 patients (utilizing 532,328 images on which the various AI models were trained) were used. The SROC was 0.94 (95% CI: 0.92–0.96). Pooled sensitivity, specificity and diagnostic odds ratio were 90.3% (95% CI: 87.1–92.7%), 84.4% (95% CI: 80.2–87.9%) and 48.1 (95% CI: 28.4–81.5), respectively. Subgroup analysis of AI models trained only on white light endoscopy was similar with pooled sensitivity and specificity of 91.2% (95% CI: 85.7–94.7%) and 85.1% (95% CI: 81.6%−88.1%), respectively.

    Conclusions: AI is highly accurate at detecting early Barrett's neoplasia and validated for patients with at least high-grade dysplasia and above. Further well-designed prospective randomized controlled studies of all histopathological subtypes of early Barrett's neoplasia are needed to confirm these findings further.

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Projects

  • The Detection Of Barrett’s Oesophagus In Endoscopic Photography

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    Barrett’s Oesophagus is diagnosed from prolonged and repeated exposure to stomach acid in the lining of the oesophagus. This paper discussed the detection of Barrett’s Oesophagus in endoscopic photography using convolutional neural network techniques. This implementation leveraged the DenseNet Convolutional Neural Network (D-CNN) architecture to determine a binary outcome sourced from endoscopic procedural labelled images. After 90 epochs, this implementation achieved a best accuracy of 99.12%.

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  • Spectre & Meltdown

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    For this project I worked closely with Dr Yuval Yarom, one of the collaborators in discovering the Spectre & Meltdown bugs. This research project investigated a proof of concept demonstrating how Spectre gadgets could be identified at run time on a target machine.

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  • Big Data - Static Visualisation

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    This project explored creating static visualisations with big data. I implemented over 65 million data entires from an online dataset, describing a games' interaction with players.

Honors & Awards

  • CFS Most Improved Firefighter 2020

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  • University of Adelaide Graduate Award

    The University of Adelaide

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