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AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.
Shonkoff, Eleanor; Cara, Kelly Copeland; Pei, Xuechen Anna; Chung, Mei; Kamath, Shreyas; Panetta, Karen; Hennessy, Erin.
Afiliación
  • Shonkoff E; School of Health Sciences, Merrimack College, North Andover, MA, USA.
  • Cara KC; Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA.
  • Pei XA; Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA.
  • Chung M; Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA.
  • Kamath S; School of Engineering, Tufts University, Medford, MA, USA.
  • Panetta K; School of Engineering, Tufts University, Medford, MA, USA.
  • Hennessy E; Friedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USA.
Ann Med ; 55(2): 2273497, 2023.
Article en En | MEDLINE | ID: mdl-38060823
These results suggest that AI methods are in line with ­ and have the potential to exceed ­ accuracy of human estimations of nutrient content based on digital food images.Variability in food image databases used and results reported prevented meta-analytic synthesis.The field can advance by testing AI architectures on a limited number of large-scale food image and nutrition databases that the field determines to be accurate and by reporting accuracy of at least absolute and relative error for volume or calorie estimations.Overall, the tools currently available need more development before deployment as stand-alone dietary assessment methods in nutrition research or clinical practice.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Evaluación Nutricional Tipo de estudio: Systematic_reviews Límite: Humans Idioma: En Revista: Ann Med Asunto de la revista: MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Inteligencia Artificial / Evaluación Nutricional Tipo de estudio: Systematic_reviews Límite: Humans Idioma: En Revista: Ann Med Asunto de la revista: MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido