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1.
PLoS One ; 19(7): e0302413, 2024.
Article in English | MEDLINE | ID: mdl-38976703

ABSTRACT

During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.


Subject(s)
COVID-19 , Deep Learning , SARS-CoV-2 , Tomography, X-Ray Computed , COVID-19/diagnostic imaging , Humans , Tomography, X-Ray Computed/methods , SARS-CoV-2/isolation & purification , Pneumonia, Viral/diagnostic imaging , Pandemics , Algorithms , Pneumonia/diagnostic imaging , Pneumonia/diagnosis , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/diagnosis , Internet , Betacoronavirus
2.
Article in English | MEDLINE | ID: mdl-38819161

ABSTRACT

PURPOSE: To describe the outcomes of acellular fish skin grafts for repair of periocular anterior lamella skin defects after Mohs surgery for skin cancers. METHODS: Following the institutional review board approval, we conducted a retrospective chart review of patients treated with acellular fish skin grafts between January 2022 and December 2023. Indication was to repair defects after Mohs excision of basal cell carcinoma and squamous cell carcinoma. Demographics, smoking and diabetes status, diagnosis, defect location, graft size, and complications were evaluated. Outcomes were analyzed using the scar cosmesis assessment and rating scale. RESULTS: Six patients (3 females and 3 males) with a mean age of 60.8 (range 44-80) had Mohs surgery for basal cell carcinoma (4) and squamous cell carcinoma (2). Location of defects included eyebrow (3 cases), lateral nasal wall (1 case), lower eyelid (1 case), and medial lower eyelid/nasal wall (1 case). Defect size ranged from 8 × 10 mm to 30 × 40 mm. Two patients had more than 1 application of xenograft. One patient developed a mild cicatricial ectropion. No other postoperative complications were seen, and all had good wound healing and cosmetically acceptable results. CONCLUSIONS: In this pilot study, acellular fish skin xenografts are shown to be promising skin graft substitutes in patients with Mohs defects and decrease the need for autologous skin harvesting or allogenic skin donation.

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