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Review of recent advances in non-invasive hemoglobin estimation

IAES International Journal of Artificial Intelligence · Vol. 14 No. 2 (2025) · hlm. 1031–1048

DOI
10.11591/ijai.v14.i2.pp1031-1048
ISSN
2252-8938, 2089-4872
Terbit
2025-04-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Hemoglobin is essential for diagnosing conditions like anemia and respiratory issues. Traditionally, the assessment of hemoglobin necessitates invasive techniques that involve blood draws, which can induce discomfort and present possible complications for patients. Recent advancements in non-invasive technologies have light-emitting diode (LED) to the development of smartphone applications and machine learning algorithms that allow real-time hemoglobin level estimation, eliminating the need for blood sampling. This not only improves patient comfort but also enhances access to ongoing health monitoring. This review aims to delve into the newest developments in smartphone-oriented strategies for hemoglobin estimation, highlighting their importance within contemporary healthcare practices and the potential implications they might have for more expansive clinical applications. Technological advancements have combined smartphones and artificial intelligence (AI) for non-invasive hemoglobin estimation, offering a promising alternative to traditional methods. These solutions optimize data collection and analysis processes, enhance diagnoses' accuracy, and facilitate timely medical interventions. Advancements in technology have revolutionized medical diagnostics, particularly in estimating hemoglobin levels non-invasively. AI methodologies have demonstrated significant results in accurately forecasting hemoglobin concentrations through a variety of analytical strategies. Future research should focus on the best configurations for these networks and the physiological concepts underpinning spectral data interpretations.

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  • University of Technology Malaysia
  • Universiti Teknikal Malaysia Melaka
  • Universitas Ahmad Dahlan
  • Universiti Malaysia Perlis
  • Islamic Azad University Yasuj
  • Diponegoro University
  • Universiti Sains Malaysia
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Rekan penulisOrganisasiArtikel bersama
Auzani JidinUniversiti Teknikal Malaysia Melaka39
Nik Rumzi Nik IdrisUniversity of Technology Malaysia31
Mohd Hatta JopriUniversiti Teknikal Malaysia Melaka20
Awang JusohUniversity of Technology Malaysia17
Mohammad JannatiUniversity of Technology Malaysia16
Mohd Junaidi Abdul AzizUniversity of Technology Malaysia16
Kasrul Abdul KarimUniversiti Teknikal Malaysia Melaka13
Mustafa ManapUniversiti Teknikal Malaysia Melaka12
Kee Jiar YeoUniversity of Technology Malaysia11
Mochammad FactaDiponegoro University11
Mohamed SalemUniversiti Sains Malaysia10
Shahreen binti KasimTun Hussein Onn University of Malaysia10
Abdul Rahim AbdullahUniversiti Teknikal Malaysia Melaka9
Aiman Zakwan JidinUniversiti Teknikal Malaysia Melaka9
Mahmoud ZadehbagheriIslamic Azad University Yasuj8
Siti Azura Ahmad TarusanUniversiti Teknikal Malaysia Melaka7
Nor Azizah Mohd YusoffUniversiti Teknikal Malaysia Melaka7
Shahrin Md AyobUniversity of Technology Malaysia7
Mohammad Faridun Naim TajuddinUniversiti Malaysia Perlis6
Ibrahim AlhamrouniUniversity of Kuala Lumpur6
Munawar Agus RiyadiDiponegoro University6
Imam Much Ibnu SubrotoSultan Agung Islamic University6
Ibrahim Mohd AlsofyaniUniversity of Technology Malaysia6
Tri WahonoAfiliasi tidak diketahui6
M. Rahimi YusoffUniversiti Teknikal Malaysia Melaka5
Shih-Hui LeeUniversity of Technology Malaysia5
Azralmukmin AzmiUniversiti Malaysia Perlis5
Norjulia Mohamad NordinUniversity of Technology Malaysia5
Muhammad AdoUniversity of Technology Malaysia4
Mohammad Javad KianiIslamic Azad University Yasuj4
Vahdat NazerianUniversity of Mazandaran4
Muhammad Zaid AihsanUniversiti Malaysia Perlis4
Chibuike Peter OhanuUniversity of Nigeria4

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Review of recent advances in non-invasive hemoglobin estimation</dc:title>
	<dc:creator>Sutikno, Tole</dc:creator>
	<dc:creator>Handayani, Lina</dc:creator>
	<dc:creator>Ruliyandari, Rochana</dc:creator>
	<dc:creator>Wijaya, Oktomi</dc:creator>
	<dc:creator>Satrian Purnama, Hendril</dc:creator>
	<dc:creator>Arsadiando, Watra</dc:creator>
	<dc:creator>Pamungkas, Anggit</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Artificial intelligence; Health monitoring; Hemoglobin; Machine learning; Non-invasive; Smartphone;</dc:subject>
	<dc:description xml:lang="en-US">Hemoglobin is essential for diagnosing conditions like anemia and respiratory issues. Traditionally, the assessment of hemoglobin necessitates invasive techniques that involve blood draws, which can induce discomfort and present possible complications for patients. Recent advancements in non-invasive technologies have light-emitting diode (LED) to the development of smartphone applications and machine learning algorithms that allow real-time hemoglobin level estimation, eliminating the need for blood sampling. This not only improves patient comfort but also enhances access to ongoing health monitoring. This review aims to delve into the newest developments in smartphone-oriented strategies for hemoglobin estimation, highlighting their importance within contemporary healthcare practices and the potential implications they might have for more expansive clinical applications. Technological advancements have combined smartphones and artificial intelligence (AI) for non-invasive hemoglobin estimation, offering a promising alternative to traditional methods. These solutions optimize data collection and analysis processes, enhance diagnoses' accuracy, and facilitate timely medical interventions. Advancements in technology have revolutionized medical diagnostics, particularly in estimating hemoglobin levels non-invasively. AI methodologies have demonstrated significant results in accurately forecasting hemoglobin concentrations through a variety of analytical strategies. Future research should focus on the best configurations for these networks and the physiological concepts underpinning spectral data interpretations.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-04-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/27028</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i2.pp1031-1048</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 2: April 2025; 1031-1048</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i2</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/27028/14430</dc:relation>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/downloadSuppFile/27028/4069</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>