Identification and Quantification of Jaundice by Trans-Conjunctiva Optical Imaging Using a Human Brain-like Algorithm: A Cross-Sectional Study

• Published in: Diagnostics Vol. 13; no. 10; p. 1767
• Main Authors: Kihara, Takuya, Sugihara, Takaaki, Ikeda, Suguru, Matsuki, Yukako, Koda, Hiroki, Onoyama, Takumi, Takata, Tomoaki, Nagahara, Takakazu, Isomoto, Hajime
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.05.2023; MDPI
• Subjects: Adults; Algorithms; Brain; Cameras; cirrhosis; Communication; conjunctiva; Cross-sectional studies; Deep learning; hyperbilirubinemia; image processing; Jaundice; Liver diseases; Medical imaging equipment; Medicine (General); Pancreatic cancer; Patients; R5-920; smartphone; Smartphones; Womens health; United States > US; Japan; image processing; smartphone; conjunctiva; hyperbilirubinemia; jaundice; biliary obstruction; cirrhosis
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13101767

Jaundice is caused by excess circulating bilirubin, known as hyperbilirubinemia. This symptom is sometimes caused by a critical hepatobiliary disorder, and is generally identified as yellowish sclera when bilirubin levels increase more than 3 mg/dL. It is difficult to identify jaundice accurately, especially via telemedicine. This study aimed to identify and quantify jaundice by trans-conjunctiva optical imaging. Patients with jaundice (total bilirubin ≥ 3 mg/dL) and normal control subjects (total bilirubin < 3 mg/dL) were prospectively enrolled from June 2021 to July 2022. We took bilateral conjunctiva imaging with a built-in camera on a smartphone (1st generation iPhone SE) under normal white light conditions without any restrictions. We processed the images using an Algorithm Based on Human Brain (ABHB) (Zeta Bridge Corporation, Tokyo, Japan) and converted them into a hue degree of Hue Saturation Lightness (HSL) color space. A total of 26 patients with jaundice (9.57 ± 7.11 mg/dL) and 25 control subjects (0.77 ± 0.35 mg/dL) were enrolled in this study. The causes of jaundice among the 18 male and 8 female subjects (median age 61 yrs.) included hepatobiliary cancer (n = 10), chronic hepatitis or cirrhosis (n = 6), pancreatic cancer (n = 4), acute liver failure (n = 2), cholelithiasis or cholangitis (n = 2), acute pancreatitis (n = 1), and Gilbert’s syndrome (n = 1). The maximum hue degree (MHD) optimal cutoff to identify jaundice was 40.8 (sensitivity 81% and specificity 80%), and the AUROC was 0.842. The MHD was moderately correlated to total serum bilirubin (TSB) levels (rS = 0.528, p < 0.001). TSB level (≥5 mg/dL) can be estimated by the formula 21.1603 − 0.7371 × 56.3−MHD2. In conclusion, the ABHB-based MHD of conjunctiva imaging identified jaundice using an ordinary smartphone without any specific attachments and deep learning. This novel technology could be a helpful diagnostic tool in telemedicine or self-medication.