| Autor: |
Nobuji Kouno, Satoshi Takahashi, Masaaki Komatsu, Yusuke Sakaguchi, Naoaki Ishiguro, Katsuji Takeda, Kyoko Fujioka, Ayumu Matsuoka, Maiko Fujimori, Ryuji Hamamoto |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Bioengineering, Vol 11, Iss 11, p 1154 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2306-5354 |
| DOI: |
10.3390/bioengineering11111154 |
| Popis: |
Objective physical function assessment is crucial for determining patient eligibility for treatment and adjusting the treatment intensity. Existing assessments, such as performance status, are not well standardized, despite their frequent use in daily clinical practice. This paper explored how artificial intelligence (AI) could predict physical function scores from various patient data sources and reviewed methods to measure objective physical function using this technology. This review included relevant articles published in English that were retrieved from PubMed. These studies utilized AI technology to predict physical function indices from patient data extracted from videos, sensors, or electronic health records, thereby eliminating manual measurements. Studies that used AI technology solely to automate traditional evaluations were excluded. These technologies are recommended for future clinical systems that perform repeated objective physical function assessments in all patients without requiring extra time, personnel, or resources. This enables the detection of minimal changes in a patient’s condition, enabling early intervention and enhanced outcomes. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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