Use of Facial Morphology to Determine Nutritional Status in Older Adults: Opportunities and Challenges

Abstract

Undiagnosed malnutrition is a significant problem in high-income countries, which can reduce the quality of life of many individuals, particularly of older adults. Moreover, it can also inflate the costs of existing health care systems because of the many metabolic complications that it can cause. The current methods for assessing malnutrition can be cumbersome. A trained practitioner must be present to conduct an assessment, or patients must travel to facilities with specialized equipment to obtain their measurements. Therefore, digital health care is a possible way of closing this gap as it is rapidly gaining traction as a scalable means of improving efficiency in the health care system. It allows for the remote monitoring of nutritional status without requiring the physical presence of practitioners or the use of advanced medical equipment. As such, there is an increasing interest in expanding the range of digital applications to facilitate remote monitoring and management of health issues. In this study, we discuss the feasibility of a novel digital remote method for diagnosing malnutrition using facial morphometrics. Many malnutrition screening assessments include subjective assessments of the head and the face. Facial appearance is often used by clinicians as the first point of qualitative indication of health status. Hence, there may be merit in quantifying these subtle but observable changes using facial morphometrics. Modern advancements in artificial intelligence, data science, sensors, and computing technologies allow facial features to be accurately digitized, which could potentially allow these previously intuitive assessments to be quantified. This study aims to stimulate further discussion and discourse on how this emerging technology can be used to provide real-time access to nutritional status. The use of facial morphometrics extends the use of currently available technology and may provide a scalable, easily deployable solution for nutritional status to be monitored in real time. This will enable clinicians and dietitians to keep track of patients remotely and provide the necessary intervention measures as required, as well as providing health care institutions and policy makers with essential information that can be used to inform and enable targeted public health approaches within affected populations.

Development and validation of a point‐of‐care nursing mobile tool to guide the diagnosis of malnutrition in hospitalized adult patients: a multicenter, prospective cohort study

“…These comprise the temporal fat pad, the preaponeurotic fat pad, the nasal fat pads, and the central fat pad. In cases of lipoatrophy, the loss of volume in these fat pads has been observed to accentuate the superior, medial, and lateral edges of the orbital rim, contributing to a sunken, skeletonized appearance 26 . Due to the limitations of camera‐captured images in providing depth information, we made the decision to use the orbital region and the rest of the face excluding the orbital region as two distinct features for inclusion in our study.…”

“…In cases of lipoatrophy, the loss of volume in these fat pads has been observed to accentuate the superior, medial, and lateral edges of the orbital rim, contributing to a sunken, skeletonized appearance. 26 Due to the limitations of camera‐captured images in providing depth information, we made the decision to use the orbital region and the rest of the face excluding the orbital region as two distinct features for inclusion in our study. Our analysis indicated that using the periorbital region as a feature yielded superior results with the same model.…”

Development and evaluation of a deep learning framework for the diagnosis of malnutrition using a 3D facial points cloud: A cross‐sectional study

“…12,26,27 The feasibility of diagnosing DRM using facial features has been validated. 28 For instance, certain bone processes and ridges, or the angulation of the jawline, may indicate there is a loss of fat or muscle tissue. [29][30][31] However, the use of facial features for malnutrition assessment has been limited by (1) the few categories and low prevalence of facial features, (2) tedious manual measurements, and (3) the lack of a golden standard measurement conducted by experienced physicians.…”

“…Many nutrition assessments include a subjective evaluation of the head and face 12,26,27 . The feasibility of diagnosing DRM using facial features has been validated 28 . For instance, certain bone processes and ridges, or the angulation of the jawline, may indicate there is a loss of fat or muscle tissue 29–31 .…”

Craniofacial Angular Photogrammetric Evaluation of Ijaws Resident in Port Harcourt, Nigeria

“…The face just like other parts of the body shows morphological variation from individual to individual. The shape of the face is determined by underlying bone, thickness and distribution of the underlying fat as well as the facial muscles [5].…”