Lip cancers are relatively rare, but early diagnosis is important for a good outcome. Unfortunately, many patients experience a delay in diagnosis. A new machine learning method, deep convolutional neural networks (DCNNs), uses algorithms which can reportedly be used to classify dermatological diseases at the same standard as board-certified dermatologists. However, this has not been verified for locations such as the lips, scalp, and genitals.A DCNN was used to classify malignant (cancerous) and benign (non-cancerous) lip disorders and its performance was evaluated. The images in this study were taken from the photo database of Seoul National University Hospital (SNUH) in South Korea. To validate the results, additional images were collected from two other affiliated hospitals. A total of 1973 lip images from SNUH were used including 853 malignant and 1120 benign diseases.The DCNN was trained with 1629 images (743 malignant, 886 benign) and its performance was evaluated using testing and external validation sets containing 344 and 281 images, respectively. For comparison, 44 participants with different levels of training were asked to classify the images.The study found that the DCNN's performance was equivalent to the dermatologists, and was superior to the nondermatologists when classifying malignancy. When they referenced the DCNN result, non-dermatologists performed significantly better.Thus, DCNNs can be used to classify lip diseases at a standard equivalent to a board-certified dermatologist and they can help unskilled physicians to discriminate between benign and malignant lip diseases. DCNNs could therefore be used to improve diagnosis and consequent patient outcomes for those with suspected lip cancers. This is a summary of the study: Dermatologist-level classification of malignant lip diseases using a deep convolutional neural network This summary relates to https://doi.
Abstract. China is confronting increasing ozone (O3) pollution that worsens air
quality and public health. Extreme O3 pollution occurs more
frequently under special events and unfavorable meteorological conditions.
Here we observed significantly elevated maximum daily 8 h average (MDA8)
O3 (up to 98 ppb) during the Chinese National Day holiday (CNDH) in
2018 throughout China, with a prominent rise by up to 120 % compared to
the previous week. The air quality model shows that increased precursor
emissions and regional transport are major contributors to the elevation. In
the Pearl River Delta region, the regional transport contributed up to 30 ppb O3 during the CNDH. Simultaneously, aggravated health risk occurs
due to high O3, inducing 33 % additional deaths throughout China.
Moreover, in tourist cities such as Sanya, daily mortality even increases
significantly from 0.4 to 1.6. This is the first comprehensive study to
investigate O3 pollution during the CNDH at the national level, aiming to
arouse more focus on the O3 holiday impact of the public.
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