A Convolutional Neural Network for Learning Local Feature Descriptors on Multispectral Images

Nunes, Cristiano; Pádua, Flávio Luis Cardeal

doi:10.1109/tla.2022.9661460

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…The most common causes included a breakdown in communication, time pressure, emergency procedures, multiple procedures on the same patient by different surgeons, and obesity. Careful pre-surgery checks were recommended to determine effective solutions [6].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Image Recognition System for Preventing Wrong-Site Upper Limb Surgery

Wu,

Chang,

Huang

et al. 2023

Diagnostics

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Our image recognition system employs a deep learning model to differentiate between the left and right upper limbs in images, allowing doctors to determine the correct surgical position. From the experimental results, it was found that the precision rate and the recall rate of the intelligent image recognition system for preventing wrong-site upper limb surgery proposed in this paper could reach 98% and 93%, respectively. The results proved that our Artificial Intelligence Image Recognition System (AIIRS) could indeed assist orthopedic surgeons in preventing the occurrence of wrong-site left and right upper limb surgery. At the same time, in future, we will apply for an IRB based on our prototype experimental results and we will conduct the second phase of human trials. The results of this research paper are of great benefit and research value to upper limb orthopedic surgery.

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Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Image Recognition System for Preventing Wrong-Site Upper Limb Surgery

Wu,

Chang,

Huang

et al. 2023

Diagnostics

View full text Add to dashboard Cite

show abstract

“…The most common causes included breakdown in communication, time pressure, emergency procedures, multiple procedures on the same patient by different surgeons, and obesity. The author recommends doing check lists and checking carefully before surgery, which is an effective solution [6].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Image Recognition System for Preventing Wrong Side Upper Limb Surgery

Wu,

Chang,

Huang

et al. 2023

Preprint

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Our image recognition system mainly judges whether the left upper limb in the image is the left upper limb or the right upper limb through our deep learning model in the image. The doctor then could give the correct surgical position. From the experimental results, it could be found that the precision rate and recall rate of the intelligent image recognition system proposed in this paper for preventing the upper limb dislocation surgery could reach 98% and 93%, respectively. It proved that our intelligent image recognition system could indeed assist orthopedic surgeons to prevent the occurrence of left and right dislocation in upper limb surgery. At the same time, this paper also completes the IRB application approval through the prototype experimental results and will conduct the second phase of human trials in the future. It showed that the research results of this paper will be of great benefit and research value to upper limb orthopedic surgery.

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Multimodal Sentiment Analysis: A GRU Approach for Improved Accuracy

Manogaran,

Annamalai,

et al. 2024

2024 International Conference on Electrical Electronics and Computing Technologies (ICEECT)

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A Convolutional Neural Network for Learning Local Feature Descriptors on Multispectral Images

Cited by 5 publications

References 30 publications

Artificial Intelligence Image Recognition System for Preventing Wrong-Site Upper Limb Surgery

Artificial Intelligence Image Recognition System for Preventing Wrong-Site Upper Limb Surgery

Intelligent Image Recognition System for Preventing Wrong Side Upper Limb Surgery

Multimodal Sentiment Analysis: A GRU Approach for Improved Accuracy

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