Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network

Abstract Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based phototherapy cabinets contain a bunch of artificial lamps, either broad-band (main emission spectrum 280–360 nm, maximum 320 nm), or narrow-ban...

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Autores principales:	R. Raksasat, P. Sri-iesaranusorn, J. Pemcharoen, P. Laiwarin, S. Buntoung, S. Janjai, E. Boontaveeyuwat, P. Asawanonda, S. Sriswasdi, E. Chuangsuwanich
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1dc2e0508aca434089c9b5e53676e277
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spelling	oai:doaj.org-article:1dc2e0508aca434089c9b5e53676e2772021-12-02T13:19:20ZAccurate surface ultraviolet radiation forecasting for clinical applications with deep neural network10.1038/s41598-021-84396-22045-2322https://doaj.org/article/1dc2e0508aca434089c9b5e53676e2772021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84396-2https://doaj.org/toc/2045-2322Abstract Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based phototherapy cabinets contain a bunch of artificial lamps, either broad-band (main emission spectrum 280–360 nm, maximum 320 nm), or narrow-band UV B irradiation (main emission spectrum 310–315 nm, maximum 311 nm). For patients who cannot access phototherapy centers, sunbathing, or heliotherapy, can be a safe and effective treatment alternative. However, as sunlight contains the full range of UV radiation (290–400 nm), careful sunbathing supervised by photodermatologist based on accurate UV radiation forecast is vital to minimize potential adverse effects. Here, using 10-year UV radiation data collected at Nakhon Pathom, Thailand, we developed a deep learning model for UV radiation prediction which achieves around 10% error for 24-h forecast and 13–16% error for 7-day up to 4-week forecast. Our approach can be extended to UV data from different geographical regions as well as various biological action spectra. This will become one of the key tools for developing national heliotherapy protocol in Thailand. Our model has been made available at https://github.com/cmb-chula/SurfUVNet .R. RaksasatP. Sri-iesaranusornJ. PemcharoenP. LaiwarinS. BuntoungS. JanjaiE. BoontaveeyuwatP. AsawanondaS. SriswasdiE. ChuangsuwanichNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q R. Raksasat P. Sri-iesaranusorn J. Pemcharoen P. Laiwarin S. Buntoung S. Janjai E. Boontaveeyuwat P. Asawanonda S. Sriswasdi E. Chuangsuwanich Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network
description	Abstract Exposure to appropriate doses of UV radiation provides enormously health and medical treatment benefits including psoriasis. Typical hospital-based phototherapy cabinets contain a bunch of artificial lamps, either broad-band (main emission spectrum 280–360 nm, maximum 320 nm), or narrow-band UV B irradiation (main emission spectrum 310–315 nm, maximum 311 nm). For patients who cannot access phototherapy centers, sunbathing, or heliotherapy, can be a safe and effective treatment alternative. However, as sunlight contains the full range of UV radiation (290–400 nm), careful sunbathing supervised by photodermatologist based on accurate UV radiation forecast is vital to minimize potential adverse effects. Here, using 10-year UV radiation data collected at Nakhon Pathom, Thailand, we developed a deep learning model for UV radiation prediction which achieves around 10% error for 24-h forecast and 13–16% error for 7-day up to 4-week forecast. Our approach can be extended to UV data from different geographical regions as well as various biological action spectra. This will become one of the key tools for developing national heliotherapy protocol in Thailand. Our model has been made available at https://github.com/cmb-chula/SurfUVNet .
format	article
author	R. Raksasat P. Sri-iesaranusorn J. Pemcharoen P. Laiwarin S. Buntoung S. Janjai E. Boontaveeyuwat P. Asawanonda S. Sriswasdi E. Chuangsuwanich
author_facet	R. Raksasat P. Sri-iesaranusorn J. Pemcharoen P. Laiwarin S. Buntoung S. Janjai E. Boontaveeyuwat P. Asawanonda S. Sriswasdi E. Chuangsuwanich
author_sort	R. Raksasat
title	Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network
title_short	Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network
title_full	Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network
title_fullStr	Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network
title_full_unstemmed	Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network
title_sort	accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/1dc2e0508aca434089c9b5e53676e277
work_keys_str_mv	AT rraksasat accuratesurfaceultravioletradiationforecastingforclinicalapplicationswithdeepneuralnetwork AT psriiesaranusorn accuratesurfaceultravioletradiationforecastingforclinicalapplicationswithdeepneuralnetwork AT jpemcharoen accuratesurfaceultravioletradiationforecastingforclinicalapplicationswithdeepneuralnetwork AT plaiwarin accuratesurfaceultravioletradiationforecastingforclinicalapplicationswithdeepneuralnetwork AT sbuntoung accuratesurfaceultravioletradiationforecastingforclinicalapplicationswithdeepneuralnetwork AT sjanjai accuratesurfaceultravioletradiationforecastingforclinicalapplicationswithdeepneuralnetwork AT eboontaveeyuwat accuratesurfaceultravioletradiationforecastingforclinicalapplicationswithdeepneuralnetwork AT pasawanonda accuratesurfaceultravioletradiationforecastingforclinicalapplicationswithdeepneuralnetwork AT ssriswasdi accuratesurfaceultravioletradiationforecastingforclinicalapplicationswithdeepneuralnetwork AT echuangsuwanich accuratesurfaceultravioletradiationforecastingforclinicalapplicationswithdeepneuralnetwork
_version_	1718393257418817536

Accurate surface ultraviolet radiation forecasting for clinical applications with deep neural network

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