scholarly journals Accuracy of Image-Based Automated Diagnosis in the Identification and Classification of Acute Burn Injuries. A Systematic Review

2021 ◽  
Vol 2 (4) ◽  
pp. 281-292
Author(s):  
Constance Boissin ◽  
Lucie Laflamme

Although they are a common type of injury worldwide, burns are challenging to diagnose, not least by untrained point-of-care clinicians. Given their visual nature, developments in artificial intelligence (AI) have sparked growing interest in the automated diagnosis of burns. This review aims to appraise the state of evidence thus far, with a focus on the identification and severity classification of acute burns. Three publicly available electronic databases were searched to identify peer-reviewed studies on the automated diagnosis of acute burns, published in English since 2005. From the 20 identified, three were excluded on the grounds that they concerned animals, older burns or lacked peer review. The remaining 17 studies, from nine different countries, were classified into three AI generations, considering the type of algorithms developed and the images used. Whereas the algorithms for burn identification have not gained much in accuracy across generations, those for severity classification improved substantially (from 66.2% to 96.4%), not least in the latest generation (n = 8). Those eight studies were further assessed for methodological bias and results applicability, using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. This highlighted the feasibility nature of the studies and their detrimental dependence on online databases of poorly documented images, at the expense of a substantial risk for patient selection and limited applicability in the clinical setting. In moving past the pilot stage, future development work would benefit from greater input from clinicians, who could contribute essential point-of-care knowledge and perspectives.

2018 ◽  
Vol 29 (16) ◽  
Author(s):  
Jung Hyun Seo ◽  
Hyang Sook Ryu ◽  
Youn Young Lee ◽  
Myeong Jong Kim ◽  
Young Soon Choi

2021 ◽  
Author(s):  
Pedro W Crous ◽  
Amy Y Rossman ◽  
Catherine Aime ◽  
Cavan Allen ◽  
Treena Burgess ◽  
...  

Names of phytopathogenic fungi and oomycetes are essential to communicate knowledge about species and their biology, control, and quarantine as well as for trade and research purposes. Many plant pathogenic fungi are pleomorphic, meaning that they produce different asexual (anamorph) and sexual (teleomorph) morphs in their lifecycles. Because of this, more than one name has been applied to different morphs of the same species, which has confused users of names. The onset of DNA technologies makes it possible to connect different morphs of the same species, resulting in a move to a more natural classification system for fungi, in which a single name for a genus as well as species can now be used. The move to a single nomenclature, as well as the advent of molecular phylogeny and the introduction of polythetic taxonomic approaches has been the main driving force for the re-classification of fungi, including pathogens. Nonetheless, finding the correct name for species remains challenging, but there is a series of steps or considerations that could greatly simplify this process, as outlined here. In addition to various online databases and resources, a list of accurate names is herewith provided of the accepted names of the most common genera and species of phytopathogenic fungi.


2018 ◽  
Vol 27 (3) ◽  
pp. 544-550 ◽  
Author(s):  
M.J.L. Mastboom ◽  
F.G.M. Verspoor ◽  
D.F. Hanff ◽  
M.G.J. Gademan ◽  
P.D.S. Dijkstra ◽  
...  

Joints ◽  
2018 ◽  
Vol 06 (02) ◽  
pp. 116-121
Author(s):  
Marco Cianforlini ◽  
Serena Ulisse ◽  
Valentino Coppa ◽  
Marco Grassi ◽  
Marco Rotini ◽  
...  

Purpose The objective of this study was to investigate the ability of elastosonography (USE) in the identification of different grades of muscular injuries, comparing its effectiveness with traditional ultrasound (US) survey and by relating the results to the clinical classification of muscular pain. Methods In the period between August 2014 and May 2016, we conducted a prospective cohort study on a population of 34 young male professional athletes belonging to the same under-17 football club (Ancona 1905). Injuries were recorded according to location, type, mechanism, recurrence, and whether they occurred with or without contact. Muscle pain was classified, after a physical examination, according to the classification of Mueller-Wohlfahrt et al. All athletes were evaluated by musculoskeletal US and USE in hours following the trauma/onset of pain. Results Seventy injuries were documented among 19 players. Muscle/tendon injuries were the most common type of injury (49%). USE showed areas of edema in nine lesions that were negative at the US examination and previously classified as fatigue-induced muscle disorders. These nine players took more time to return to physical activity compared with others with injuries classified into the same group, but negative at USE evaluation. Conclusion USE is a valuable aid in the diagnosis and prognostic evaluation of muscle injury, as it detects pathologic changes that are not visible with the B-mode US. Level of Evidence This is a Level III, observational cohort study.


2020 ◽  
Vol 9 (4) ◽  
pp. 1-17
Author(s):  
Mridu Sahu ◽  
Tushar Jani ◽  
Maski Saijahnavi ◽  
Amrit Kumar ◽  
Upendra Chaurasiya ◽  
...  

Rust detection is necessary for proper working and maintenance of machines for security purposes. Images are one of the suggested platforms for rust detection in which rust can be detected even though the human can't reach to the area. However, there are a lack of online databases available that can provide a sizable dataset to identify the most suitable model that can be used further. This paper provides a data augmentation technique by using Perlin noise, and further, the generated images are tested on standard features (i.e., statistical values, entropy, along with SIFT and SURF methods). The two most generalized classifiers, naïve Bayes and support vector machine, are identified and tested to obtain the performance of classification of rusty and non-rusty images. The support vector machine provides better classification accuracy, which also suggests that that the combined features of statistics, SIFT, and SURF are able to differentiate the images. Hence, it can be further used to detect the rust in different parts of machines.


1991 ◽  
Vol 11 (4_suppl) ◽  
pp. S41-S45 ◽  
Author(s):  
Frank W. Stitt ◽  
Ying Lu ◽  
Gordon M. Dickinson ◽  
Nancy G. Klimas

To validate an automated AIDS severity-of-illness prognostic algorithm, 2,113 discharge summaries of HIV-infected patients were merged with the Problem-Oriented Medical Synopsis (POMS) and an HIV risk registry. The combination of a medically derived classification and staging algorithm with multivariate statistical techniques was used for automated severity-of-illness disease staging and prognostic assignment. The model correctly predicted the outcomes of 82% of all cases (death, survivorship) at discharge, and 66% of deaths.


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