Publications by authors named "Marc Souchaud"
Article Synopsis
- Deep learning (DL) algorithms effectively detect vascular lesions in small bowel capsule endoscopy (CE), improving diagnostic performance and reducing reading time.
- A machine learning (ML) classifier was used to enhance the DL algorithm's performance by categorizing vascular abnormalities and selecting the most relevant images for reporting.
- The random forest (RF) method achieved high specificity (91.1%) and accuracy (84.2%) in distinguishing significant lesions while dramatically reducing the number of images reported, demonstrating potential for automated CE reporting without sacrificing diagnostic quality.
Several Diptera species are known to transmit pathogens of medical and veterinary interest. However, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. A computer vision-based system that uses Wing interferential patterns (WIPs) to identify these insects could solve this problem.
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- Sandflies are important vectors for various pathogens, making their accurate identification crucial for public health and veterinary concerns.
- Traditional methods of identifying sandflies are difficult due to the need to examine tiny internal structures, leading to the development of a new technique using Wing Interferential Patterns (WIPs) and deep learning.
- This innovative approach achieves over 77% accuracy in classifying sandflies across different taxonomic levels without needing to look at internal organs, making it suitable for fieldwork and addressing the shortage of medical entomologists.
Hematophagous insects belonging to the Aedes genus are proven vectors of viral and filarial pathogens of medical interest. Aedes albopictus is an increasingly important vector because of its rapid worldwide expansion. In the context of global climate change and the emergence of zoonotic infectious diseases, identification tools with field application are required to strengthen efforts in the entomological survey of arthropods with medical interest.
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- A new deep learning method has been developed to identify and classify 20 species of Anopheles mosquitoes, including 13 that spread malaria.
- The method achieved up to 100% accuracy for ten species, though it showed less accuracy for some others, especially those that are difficult to distinguish.
- This tool is expected to improve malaria vector surveys and control measures, with potential applications for other diseases spread by arthropods in the future.
Article Synopsis
- A new method for identifying Glossina species is crucial for controlling Human and Animal African Trypanosomiasis without relying on a few experts.
- Current techniques like DNA barcoding and mass spectrometry are expensive and not well-suited for fieldwork.
- The study introduces a cost-effective method using Wing Interference Patterns (WIPs) with a deep learning model, showing high accuracy in recognizing 23 Glossina species using standard microscopes.
Background: Bubbles often mask the mucosa during capsule endoscopy (CE). Clinical scores assessing the cleanliness and the amount of bubbles in the small bowel (SB) are poorly reproducible unlike machine learning (ML) solutions. We aimed to measure the amount of bubbles with ML algorithms in SB CE recordings, and compare two polyethylene glycol (PEG)-based preparations, with and without simethicone, in patients with obscure gastro-intestinal bleeding (OGIB).
View Article and Find Full Text PDFBackground And Aims: Current artificial intelligence (AI)-based solutions for capsule endoscopy (CE) interpretation are proprietary. We aimed to evaluate an AI solution trained on a specific CE system (Pillcam®, Medtronic) for the detection of angiectasias on images captured by a different proprietary system (MiroCam®, Intromedic).
Material And Methods: An advanced AI solution (Axaro®, Augmented Endoscopy), previously trained on Pillcam® small bowell images, was evaluated on independent datasets with more than 1200 Pillcam® and MiroCam® still frames (equally distributed, with or without angiectasias).
Background: Cleanliness scores in small-bowel capsule endoscopy (SBCE) have poor reproducibility. The aim of this study was to evaluate a neural network-based algorithm for automated assessment of small-bowel cleanliness during capsule endoscopy.
Methods: 600 normal third-generation SBCE still frames were categorized as "adequate" or "inadequate" in terms of cleanliness by three expert readers, according to a 10-point scale, and served as a training database.