FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3206234077> ?p ?o ?g. }

• W3206234077 endingPage "e22798" @default.
• W3206234077 startingPage "e22798" @default.
• W3206234077 abstract "Accurate assessment of the percentage total body surface area (%TBSA) of burn wounds is crucial in the management of burn patients. The resuscitation fluid and nutritional needs of burn patients, their need for intensive unit care, and probability of mortality are all directly related to %TBSA. It is difficult to estimate a burn area of irregular shape by inspection. Many articles have reported discrepancies in estimating %TBSA by different doctors.We propose a method, based on deep learning, for burn wound detection, segmentation, and calculation of %TBSA on a pixel-to-pixel basis.A 2-step procedure was used to convert burn wound diagnosis into %TBSA. In the first step, images of burn wounds were collected from medical records and labeled by burn surgeons, and the data set was then input into 2 deep learning architectures, U-Net and Mask R-CNN, each configured with 2 different backbones, to segment the burn wounds. In the second step, we collected and labeled images of hands to create another data set, which was also input into U-Net and Mask R-CNN to segment the hands. The %TBSA of burn wounds was then calculated by comparing the pixels of mask areas on images of the burn wound and hand of the same patient according to the rule of hand, which states that one's hand accounts for 0.8% of TBSA.A total of 2591 images of burn wounds were collected and labeled to form the burn wound data set. The data set was randomly split into training, validation, and testing sets in a ratio of 8:1:1. Four hundred images of volar hands were collected and labeled to form the hand data set, which was also split into 3 sets using the same method. For the images of burn wounds, Mask R-CNN with ResNet101 had the best segmentation result with a Dice coefficient (DC) of 0.9496, while U-Net with ResNet101 had a DC of 0.8545. For the hand images, U-Net and Mask R-CNN had similar performance with DC values of 0.9920 and 0.9910, respectively. Lastly, we conducted a test diagnosis in a burn patient. Mask R-CNN with ResNet101 had on average less deviation (0.115% TBSA) from the ground truth than burn surgeons.This is one of the first studies to diagnose all depths of burn wounds and convert the segmentation results into %TBSA using different deep learning models. We aimed to assist medical staff in estimating burn size more accurately, thereby helping to provide precise care to burn victims." @default.
• W3206234077 created "2021-10-25" @default.
• W3206234077 creator A5000280663 @default.
• W3206234077 creator A5013325352 @default.
• W3206234077 creator A5017526842 @default.
• W3206234077 creator A5027856340 @default.
• W3206234077 creator A5034263115 @default.
• W3206234077 creator A5043297858 @default.
• W3206234077 creator A5063267255 @default.
• W3206234077 creator A5074240834 @default.
• W3206234077 creator A5084662822 @default.
• W3206234077 date "2021-12-02" @default.
• W3206234077 modified "2023-10-11" @default.
• W3206234077 title "Deep Learning–Assisted Burn Wound Diagnosis: Diagnostic Model Development Study" @default.
• W3206234077 cites W1040364392 @default.
• W3206234077 cites W1974557101 @default.
• W3206234077 cites W2027444561 @default.
• W3206234077 cites W2028190305 @default.
• W3206234077 cites W2038569144 @default.
• W3206234077 cites W2041756819 @default.
• W3206234077 cites W2048962927 @default.
• W3206234077 cites W2054430621 @default.
• W3206234077 cites W2094873648 @default.
• W3206234077 cites W2105251964 @default.
• W3206234077 cites W2115716767 @default.
• W3206234077 cites W2143016478 @default.
• W3206234077 cites W2253655339 @default.
• W3206234077 cites W2331507674 @default.
• W3206234077 cites W2344361745 @default.
• W3206234077 cites W2410584580 @default.
• W3206234077 cites W2465775424 @default.
• W3206234077 cites W2510015186 @default.
• W3206234077 cites W2566079294 @default.
• W3206234077 cites W2611204894 @default.
• W3206234077 cites W2761729144 @default.
• W3206234077 cites W2791017387 @default.
• W3206234077 cites W2797624406 @default.
• W3206234077 cites W2802597987 @default.
• W3206234077 cites W2810535676 @default.
• W3206234077 cites W2898364767 @default.
• W3206234077 cites W2911823761 @default.
• W3206234077 cites W2912415791 @default.
• W3206234077 cites W2916412824 @default.
• W3206234077 cites W2920326761 @default.
• W3206234077 cites W2922848531 @default.
• W3206234077 cites W2925489996 @default.
• W3206234077 cites W2933603317 @default.
• W3206234077 cites W2946074429 @default.
• W3206234077 cites W2949088970 @default.
• W3206234077 cites W2963150697 @default.
• W3206234077 cites W2965338984 @default.
• W3206234077 cites W3012640303 @default.
• W3206234077 cites W3033382446 @default.
• W3206234077 cites W3035604179 @default.
• W3206234077 cites W3036832166 @default.
• W3206234077 cites W3107794213 @default.
• W3206234077 cites W3127057363 @default.
• W3206234077 cites W639708223 @default.
• W3206234077 cites W926893388 @default.
• W3206234077 doi "https://doi.org/10.2196/22798" @default.
• W3206234077 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34860674" @default.
• W3206234077 hasPublicationYear "2021" @default.
• W3206234077 type Work @default.
• W3206234077 sameAs 3206234077 @default.
• W3206234077 citedByCount "5" @default.
• W3206234077 countsByYear W32062340772023 @default.
• W3206234077 crossrefType "journal-article" @default.
• W3206234077 hasAuthorship W3206234077A5000280663 @default.
• W3206234077 hasAuthorship W3206234077A5013325352 @default.
• W3206234077 hasAuthorship W3206234077A5017526842 @default.
• W3206234077 hasAuthorship W3206234077A5027856340 @default.
• W3206234077 hasAuthorship W3206234077A5034263115 @default.
• W3206234077 hasAuthorship W3206234077A5043297858 @default.
• W3206234077 hasAuthorship W3206234077A5063267255 @default.
• W3206234077 hasAuthorship W3206234077A5074240834 @default.
• W3206234077 hasAuthorship W3206234077A5084662822 @default.
• W3206234077 hasBestOaLocation W32062340771 @default.
• W3206234077 hasConcept C108583219 @default.
• W3206234077 hasConcept C141071460 @default.
• W3206234077 hasConcept C154945302 @default.
• W3206234077 hasConcept C160633673 @default.
• W3206234077 hasConcept C2776782833 @default.
• W3206234077 hasConcept C2780269544 @default.
• W3206234077 hasConcept C2993242423 @default.
• W3206234077 hasConcept C3019879682 @default.
• W3206234077 hasConcept C41008148 @default.
• W3206234077 hasConcept C71924100 @default.
• W3206234077 hasConceptScore W3206234077C108583219 @default.
• W3206234077 hasConceptScore W3206234077C141071460 @default.
• W3206234077 hasConceptScore W3206234077C154945302 @default.
• W3206234077 hasConceptScore W3206234077C160633673 @default.
• W3206234077 hasConceptScore W3206234077C2776782833 @default.
• W3206234077 hasConceptScore W3206234077C2780269544 @default.
• W3206234077 hasConceptScore W3206234077C2993242423 @default.
• W3206234077 hasConceptScore W3206234077C3019879682 @default.
• W3206234077 hasConceptScore W3206234077C41008148 @default.
• W3206234077 hasConceptScore W3206234077C71924100 @default.
• W3206234077 hasIssue "12" @default.

• next