History of dermatology: the study of skin diseases over the centuries

Ferreira, Iago Gonçalves; Weber, Magda Blessmann; Bonamigo, Renan Rangel

doi:10.1016/j.abd.2020.09.006

Cited by 26 publications

(21 citation statements)

References 53 publications

(110 reference statements)

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“…DL models are used for making predictions based on previous observations [ 6 , 29 ]. Several DL algorithms are available to analyze large datasets; through such analyses, complex and heterogeneous data can inform real-world clinical practice and recommendations [ 30 , 31 , 32 , 33 , 34 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Chen

Chin

2022

Diagnostics

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Background: Deep neck infection (DNI) can lead to airway obstruction. Rather than intubation, some patients need tracheostomy to secure the airway. However, no study has used deep learning (DL) artificial intelligence (AI) to predict the need for tracheostomy in DNI patients. Thus, the purpose of this study was to develop a DL framework to predict the need for tracheostomy in DNI patients. Methods: 392 patients with DNI were enrolled in this study between August 2016 and April 2022; 80% of the patients (n = 317) were randomly assigned to a training group for model validation, and the remaining 20% (n = 75) were assigned to the test group to determine model accuracy. The k-nearest neighbor method was applied to analyze the clinical and computed tomography (CT) data of the patients. The predictions of the model with regard to the need for tracheostomy were compared with actual decisions made by clinical experts. Results: No significant differences were observed in clinical or CT parameters between the training group and test groups. The DL model yielded a prediction accuracy of 78.66% (59/75 cases). The sensitivity and specificity values were 62.50% and 80.60%, respectively. Conclusions: We demonstrated a DL framework to predict the need for tracheostomy in DNI patients based on clinical and CT data. The model has potential for clinical application; in particular, it may assist less experienced clinicians to determine whether tracheostomy is necessary in cases of DNI.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Chen

Chin

2022

Diagnostics

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“…We passionately studied the article named “History of dermatology: the study of skin diseases over the centuries”. 1 In our view, this is an outstanding manuscript that will be acclaimed by scholars and readers.…”

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confidence: 91%

Letter to the Editor regarding: “History of dermatology: the study of skin diseases over the centuries”

Mirzaei

Ghazi-Sha’rbaf

Mohammadinasab

2021

Anais Brasileiros de Dermatologia

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“…Different artificial intelligence and machine learning technologies have been used, especially in the past two decades, to advance medicine in general, whether in diagnosis or treatment [ 4 ]. Furthermore, they have been applied in many domains, such as power and electricity markets and renewable energy [ 5 – 9 ].…”

Section: Introductionmentioning

confidence: 99%

Skin lesion classification system using a K-nearest neighbor algorithm

Hatem

2022

Vis. Comput. Ind. Biomed. Art

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One of the most critical steps in medical health is the proper diagnosis of the disease. Dermatology is one of the most volatile and challenging fields in terms of diagnosis. Dermatologists often require further testing, review of the patient’s history, and other data to ensure a proper diagnosis. Therefore, finding a method that can guarantee a proper trusted diagnosis quickly is essential. Several approaches have been developed over the years to facilitate the diagnosis based on machine learning. However, the developed systems lack certain properties, such as high accuracy. This study proposes a system developed in MATLAB that can identify skin lesions and classify them as normal or benign. The classification process is effectuated by implementing the K-nearest neighbor (KNN) approach to differentiate between normal skin and malignant skin lesions that imply pathology. KNN is used because it is time efficient and promises highly accurate results. The accuracy of the system reached 98% in classifying skin lesions.

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History of dermatology: the study of skin diseases over the centuries

Cited by 26 publications

References 53 publications

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings—Preliminary Data and a Pilot Study

Letter to the Editor regarding: “History of dermatology: the study of skin diseases over the centuries”

Skin lesion classification system using a K-nearest neighbor algorithm

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