Page 4 4. AI-Powered Assessment in Infants and Cognitively Impaired Populations
The proprieties of AI systems can be implemented to assess pain levels and guide treatment, particularly in special populations like infants and the elderly, where pain is challenging to evaluate accurately. Infants cannot verbally communicate their pain, often leading to under recognition and inadequate treatment. This inaccurate pain management in infants is linked to behavioral issues, heightened vigilance, and potential structural brain changes that impact development and learning [32]. To address these challenges, AI techniques analyze behavioral responses such as facial expressions [33], crying sounds [34], and body movements [35], as well as physiological signals like pupil dilation [36], skin conductance [37], heart rate variability [38], and cerebral hemodynamics [39]. Multimodal approaches combine these data sources for more accurate assessments [40]. For example, the PainChek Infant, a mHealth solution, uses AI to evaluate pain intensity based on facial expressions, demonstrating effectiveness in ease of use and accuracy [41]. Similarly, Carlini et al. [42] worked on the UNIFESP [43] and the Classification of Pain Expression (iCOPE) [44] repositories and developed a mobile app utilizing a convolutional neural network (CNN)-based architecture to classify neonate facial expressions as indicative of pain or not, with low latency and offline functionality. Another group of researchers evaluated EGG, an AI-powered interactive toy developed to assess individual pain levels in children. This device engages young patients through an immersive experience incorporating visual, tactile, and auditory stimuli [45]. Additionally, Gholami et al. [46] used ML for evaluating neonate pain intensity through digital imaging analyses.
For cognitively impaired elderly individuals, AI tools analyze non-verbal cues and l physiological signals to provide objective pain assessments [47]. The PainChek application is also used for dementia patients to evaluate pain through facial expressions. For example, a retrospective study by Atee et al. [48] examined facial expressions in 3,144 individuals with dementia using the PainChek Face domain. The study identified facial action units (AUs) associated with pain intensity, finding AU7 (eyelid tightening) most prevalent during severe pain. Eye-related AUs were more common at higher pain levels than mouth-related AUs. In another investigation, Babicova et al. [49] tested the tool in UK aged care residents with advanced dementia Additionally, video recordings of non-communicative patients during routine activities were analyzed to observe pain behaviors, with ratings performed
using the PAINAD score [50]. However, real-world applications of these models have shown mixed results in performance metrics [51].
The main applications are reported in Table 1.[/B]
