Rapid and noninvasive estimation of human arsenic exposure based on 4-photo-set of the hand and foot photos through artificial intelligence.

Autor: Hsu BW; Department of Computer Science, National Yang Ming Chiao Tung University, Engineering Bldg 3, 1001 University Road, Hsinchu 300, Taiwan., Hsiao WW; Department of Computer Science, National Yang Ming Chiao Tung University, Engineering Bldg 3, 1001 University Road, Hsinchu 300, Taiwan., Liu CY; Department of Dermatology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, 123 Dapi Road, Niasong District, Kaohsiung City, Taiwan 83301., Tseng VS; Department of Computer Science, National Yang Ming Chiao Tung University, Engineering Bldg 3, 1001 University Road, Hsinchu 300, Taiwan. Electronic address: vtseng@cs.nycu.edu.tw., Lee CH; Department of Dermatology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, 123 Dapi Road, Niasong District, Kaohsiung City, Taiwan 83301. Electronic address: dermlee@gmail.com.
Jazyk: angličtina
Zdroj: Journal of hazardous materials [J Hazard Mater] 2024 Dec 05; Vol. 480, pp. 136003. Date of Electronic Publication: 2024 Oct 02.
DOI: 10.1016/j.jhazmat.2024.136003
Abstrakt: Chronic exposure to arsenic is linked to the development of cancers in the skin, lungs, and bladder. Arsenic exposure manifests as variegated pigmentation and characteristic pitted keratosis on the hands and feet, which often precede the onset of internal cancers. Traditionally, human arsenic exposure is estimated through arsenic levels in biological tissues; however, these methods are invasive and time-consuming. This study aims to develop a noninvasive approach to predict arsenic exposure using artificial intelligence (AI) to analyze photographs of hands and feet. By incorporating well water consumption data and arsenic concentration levels, we developed an AI algorithm trained on 9988 hand and foot photographs from 2497 subjects. This algorithm correlates visual features of palmoplantar hyperkeratosis with arsenic exposure levels. Four pictures per patient, capturing both ventral and dorsal aspects of hands and feet, were analyzed. The AI model utilized existing arsenic exposure data, including arsenic concentration (AC) and cumulative arsenic exposure (CAE), to make binary predictions of high and low arsenic exposure. The AI model achieved an optimal area under the curve (AUC) values of 0.813 for AC and 0.779 for CAE. Recall and precision metrics were 0.729 and 0.705 for CAE, and 0.750 and 0.763 for AC, respectively. While biomarkers have traditionally been used to assess arsenic exposure, efficient noninvasive methods are lacking. To our knowledge, this is the first study to leverage deep learning for noninvasive arsenic exposure assessment. Despite challenges with binary classification due to imbalanced and sparse data, this approach demonstrates the potential for noninvasive estimation of arsenic concentration. Future studies should focus on increasing data volume and categorizing arsenic concentration statistics to enhance model accuracy. This rapid estimation method could significantly contribute to epidemiological studies and aid physicians in diagnosis.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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