Non-invasive and non-contact automatic jaundice detection of infants based on random forest

Khanam, Fatema-Tuz-Zohra; Al-Naji, Ali; Perera, Asanka G.; Wang, Danyi; Chahl, Javaan

doi:10.1080/21681163.2023.2244601

Cited by 2 publications

(1 citation statement)

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“…In another study on image processing for neonatal detection, Althnian et al investigated the effectiveness of transfer learning using the skin, eye, and a fusion of skin and eye features [73]. The transfer learning was tested against traditional machine learning models, including a multi-layer perceptron (MLP), a support vector machine (SVM), a decision tree (DT), and random forest (RF).…”

Section: Optical Biosensorsmentioning

confidence: 99%

Development of Non-Invasive Biosensors for Neonatal Jaundice Detection: A Review

Hazarika,

Borah,

Gogoi

et al. 2024

Biosensors

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One of the most common problems many babies encounter is neonatal jaundice. The symptoms are yellowing of the skin or eyes because of bilirubin (from above 2.0 to 2.5 mg/dL in the blood). If left untreated, it can lead to serious neurological complications. Traditionally, jaundice detection has relied on invasive blood tests, but developing non-invasive biosensors has provided an alternative approach. This systematic review aims to assess the advancement of these biosensors. This review discusses the many known invasive and non-invasive diagnostic modalities for detecting neonatal jaundice and their limitations. It also notes that the recent research and development on non-invasive biosensors for neonatal jaundice diagnosis is still in its early stages, with the majority of investigations being in vitro or at the pre-clinical level. Non-invasive biosensors could revolutionize neonatal jaundice detection; however, a number of issues still need to be solved before this can happen. These consist of in-depth validation studies, affordable and user-friendly gadgets, and regulatory authority approval. To create biosensors that meet regulatory requirements, additional research is required to make them more precise and affordable.

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Section: Optical Biosensorsmentioning

confidence: 99%