한빛사
>
한빛사논문
한빛사논문
- 조회197
npj Digit. Med., 10 March 2025 | https://doi.org/10.1038/s41746-025-01548-8
Smartphone conjunctiva photography for malaria risk stratification in asymptomatic school age children
Shaun G. Hong1, Sang Mok Park1, Semin Kwon1, Haripriya Sakthivel1, Sreeram P. Nagappa1, Jung Woo Leem1, Steven R. Steinhubl1,2, Pascal Ngiruwonsanga3, Jean-Louis N. Mangara4, Célestin Twizere5 & Young L. Kim1,2,6,7
1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
2Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA.
3Gakoma District Hospital, Gisagara District, Southern Province, Rwanda.
4Malaria, Neglected Tropical Diseases and Other Parasitic Diseases Division, Rwanda Biomedical Center, Kigali, Rwanda.
5Center of Excellence in Biomedical Engineering and eHealth, University of Rwanda, Kigali, Rwanda.
6Purdue Institute for Cancer Research, Purdue University, West Lafayette, IN, USA.
7Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN, USA.
Corresponding author
Correspondence to Young L. Kim.
1Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
2Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA.
3Gakoma District Hospital, Gisagara District, Southern Province, Rwanda.
4Malaria, Neglected Tropical Diseases and Other Parasitic Diseases Division, Rwanda Biomedical Center, Kigali, Rwanda.
5Center of Excellence in Biomedical Engineering and eHealth, University of Rwanda, Kigali, Rwanda.
6Purdue Institute for Cancer Research, Purdue University, West Lafayette, IN, USA.
7Purdue Quantum Science and Engineering Institute, Purdue University, West Lafayette, IN, USA.
Corresponding author
Correspondence to Young L. Kim.
Abstract
Malaria remains a major global health challenge. Although effective control relies on testing all suspected cases, asymptomatic infections in school-age children are frequently overlooked. Advances in retinal imaging and computer vision have enhanced malaria detection. However, noninvasive, point-of-care malaria detection remains unrealized, partly because of the need for specialized equipment. Here we report radiomic analyses of 4302 photographs of the palpebral conjunctiva captured using unmodified smartphone cameras from asymptomatic 405 participants aged 5 to 15 years to predict malaria risk. Our neural network classification model of radiomic features achieves an area under the receiver operating characteristic curve of 0.76 with 95% confidence intervals from 0.68 to 0.84 in distinguishing between malaria-infected and non-infected cases in endemic regions. Photographing the inner eyelid provides the advantages of easy accessibility and direct exposure to the microvasculature. This mobile health approach has the potential for malaria prescreening and managing febrile illness in resource-limited settings.
논문정보
- Research article
- 2025년 03월 (BRIC 등록일 2025-03-11)
- 국외 연구진
- 바이오·의료융합 > 의료기기
댓글 작성 로그인
팝업 닫기관련 링크
연구자 키워드
연구자 ID
관련분야 연구자보기
소속기관 논문보기
관련분야 논문보기
해당논문 저자보기
