TY - GEN
T1 - Low-cost Visible Reflectance Spectrophotometer for Classification of Small Intestine Cancer Lesion Degree
AU - Putra, Wira Tirta Dwi
AU - Tedjo, Aryo
AU - Ramadhian, Dimas
AU - Kusmardi,
N1 - Funding Information:
This study is supported by the grant of Publikasi Terindeks Internasional (PUTI) Saintekes Universitas Indonesia 2020 No: NKB-4651/UN2.RST/HKP.05.00/2020
Publisher Copyright:
© 2022 American Institute of Physics Inc.. All rights reserved.
PY - 2022/8/16
Y1 - 2022/8/16
N2 - Some studies demonstrated the application of visible light reflectance spectrophotometry in cancer diagnoses, such as skin cancer and an oral lesion. This method is quantified, able to be digitalized, affordable, and easy to use. However, the use of visible light spectrophotometry has not been used for small intestine cancer lesions. This pilot study aims to evaluate the potency of a simple or low-cost visible light reflectance spectrophotometer to classify mice's small intestine cancer lesion degree based on intensity measurement. This analytical cross-sectional study used paraffin block to preserve Mus musculus mice's small intestine tissue (normal, pre-cancer, and cancer). The samples were grouped according to the lesion degree that a pathology expert had evaluated. The best machine learning to classify lesion degree based on light intensity was performed by Support Vector Machine (SVM) with accuracy 85.2%, AUC 93, 4%, and precision of 85.3%.
AB - Some studies demonstrated the application of visible light reflectance spectrophotometry in cancer diagnoses, such as skin cancer and an oral lesion. This method is quantified, able to be digitalized, affordable, and easy to use. However, the use of visible light spectrophotometry has not been used for small intestine cancer lesions. This pilot study aims to evaluate the potency of a simple or low-cost visible light reflectance spectrophotometer to classify mice's small intestine cancer lesion degree based on intensity measurement. This analytical cross-sectional study used paraffin block to preserve Mus musculus mice's small intestine tissue (normal, pre-cancer, and cancer). The samples were grouped according to the lesion degree that a pathology expert had evaluated. The best machine learning to classify lesion degree based on light intensity was performed by Support Vector Machine (SVM) with accuracy 85.2%, AUC 93, 4%, and precision of 85.3%.
KW - Mus musculus
KW - paraffin block preserve
KW - reflectance spectrophotometry
KW - small intestine cancer
KW - visible light spectrophotometer
UR - http://www.scopus.com/inward/record.url?scp=85138249071&partnerID=8YFLogxK
U2 - 10.1063/5.0098174
DO - 10.1063/5.0098174
M3 - Conference contribution
AN - SCOPUS:85138249071
T3 - AIP Conference Proceedings
BT - 6th Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, and Medical Devices
A2 - Rahman, Siti Fauziyah
A2 - Zakiyuddin, Ahmad
A2 - Whulanza, Yudan
A2 - Intan, Nurul
PB - American Institute of Physics Inc.
T2 - 6th International Symposium of Biomedical Engineering''s Recent Progress in Biomaterials, Drugs Development, and Medical Devices, ISBE 2021
Y2 - 7 July 2021 through 8 July 2021
ER -
