Background: Chronic wounds are being treated with bioengineering skin constructs. Yet, there is no standard way of assessing these wounds. We developed a classification system to evaluate wounds after construct application. The classification system evaluates the early clinical effect of bioengineered skin and early construct appearance giving a total score named the skin substitute score.
Objective: Apply classification system to both venous and diabetic foot ulcers and determine whether classification system has validity and predictability for healing.
Methods: Evaluated serial photographs in 83 and 78 patients with diabetic foot ulcers and in 84 and 83 patients with venous ulcer on Days 7 and 14, respectively, treated with a bilayered bioengineered skin construct. Applied the classification system and determined the percentages of healed patients.
Results: There was a significant correlation between better skin substitute score and complete wound closure for both venous ulcers p=0.002 on Day 7 and p=0.01 on Day 14) and diabetic foot ulcers p=0.0005 on Day 7 and p<0.0001 on Day 14).
Conclusion: Optimal clinical effect was associated with complete wound closure. As the clinical effect becomes less than optimal continued clinical persistence of the construct becomes important. This classification system seems to have validity in predicting complete wound closure in wounds treated with a bilayered bioengineered skin construct.
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http://dx.doi.org/10.1111/j.1524-4725.2004.30334.x | DOI Listing |
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The School of Nursing, Fujian Medical University, No. 1 Xuefu North Road, Fuzhou, 350122, Fujian, China.
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National Centre for Diseases Prevention and Health Promotion, Istituto Superiore di Sanità, Rome, Italy.
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Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine.
Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.
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Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Canada.
Accurate diagnosis of oral lesions, early indicators of oral cancer, is a complex clinical challenge. Recent advances in deep learning have demonstrated potential in supporting clinical decisions. This paper introduces a deep learning model for classifying oral lesions, focusing on accuracy, interpretability, and reducing dataset bias.
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School of Engineering and Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Petaling Jaya, 47500, Selangor Darul Ehsan, Malaysia.
Cervical cancer is a deadly disease in women globally. There is a greater chance of getting rid of cervical cancer in case of earliest diagnosis. But for some patients, there is a chance of recurrence.
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