=Paper=
{{Paper
|id=Vol-3762/562
|storemode=property
|title=AI-driven technologies in Digital Health & Well Being: early detection and intervention strategies
|pdfUrl=https://ceur-ws.org/Vol-3762/562.pdf
|volume=Vol-3762
|authors=Ilaria Amaro,Alessia Auriemma Citaralla,Fabiola De Marco,Attilio Della Greca,Luigi Di Biasi,Rita Francese,Domenico Rossi,Genoveffa Tortora,Cesare Tucci
|dblpUrl=https://dblp.org/rec/conf/ital-ia/AmaroCMGBFRTT24
}}
==AI-driven technologies in Digital Health & Well Being: early detection and intervention strategies==
https://ceur-ws.org/Vol-3762/562.pdf
AI-driven technologies in Digital Health & Well Being: early
detection and intervention strategies
Ilaria Amaro, Alessia Auriemma Citarella, Fabiola De Marco*,† , Attilio Della Greca,
Luigi Di Biasi, Rita Francese, Domenico Rossi, Genoveffa Tortora and Cesare Tucci
1
University of Salerno, CAIS Lab, Department of Computer Science, Italy
Abstract
Artificial Intelligence (AI) is increasingly central to scientific research, especially in digital health and well-being. Technological
advancements enable early detection of health issues and personalized treatments, facilitating real-time monitoring, promoting
healthy lifestyles, and providing rehabilitation aids. The adoption of AI technologies requires reliability and promotes the
development of symbiotic artificial intelligence systems, wherein humans and AI collaborate synergistically. This paper
provides valuable insights into the study areas explored by our team. Our primary focus has centered on employing AI and
explainable AI (XAI)-based methodologies to enable early detection and decision-making processes regarding treatments and
rehabilitation for conditions such as skin melanoma, heart disease, and neurological disorders. It is crucial to recognize the
importance of symbiotic systems and diagnostic support tools that rely on reliable technologies such as AI and XAI. The
integration of these technologies not only improves the effectiveness of treatments and rehabilitation but also promotes
greater transparency and understanding in the decision-making processes, underscoring their crucial role in the future of
healthcare.
Keywords
Artificial Intelligence, eXplainable Artificial Intelligence, Symbiotic Artificial Intelligence, Mental Health, Alzheimer disease
1. Introduction has drawn attention to the critical role of Explainable AI
(XAI) in ensuring transparency and trustworthiness in
Artificial Intelligence (AI) is becoming increasingly the decision-making processes of these systems [1]. The
prominent in scientific study, particularly in the field practical applications of AI in digital health and wellness
of digital health and well-being. Indeed, the European are heterogeneous. Among these emerge: 1) Early de-
community, recognizing the importance of the conver- tection and intervention: analyzing extensive data aids
gence of technology and health, has adopted substantial in identifying precursor signs, enabling timely interven-
investments to promote the development of cutting-edge tions; 2) Personalized treatment: AI tailors treatment
technologies that leverage AI to improve the quality of plans to individual characteristics, enhancing effective-
life and overall health of the population. Recent deploy- ness and patient satisfaction; 3) Real-time monitoring:
ments under the coordinated AI framework make possi- continuous vital sign monitoring provides instant data
ble the adoption of a new generation of technologies in for informed decision-making. 4) Promotion of healthy
digital wellness, with an emphasis on health. Thanks to lifestyles: AI offers personalized advice through apps
rapid advances in AI, computer systems have acquired and devices, fostering better habits. 5) Rehabilitation: AI
analytical and predictive capabilities that can facilitate assists in designing tailored rehabilitation programs, and
early detection of potential health problems by helping improving outcomes.
to identify risky conditions while avoiding pathological Despite the significant inherent benefits, accepting
degeneration. In parallel, the use of these technologies and adopting AI-driven technologies in the digital health
and wellness fields may need more awareness and trust
Ital-IA 2024: 4th National Conference on Artificial Intelligence, orga-
nized by CINI, May 29-30, 2024, Naples, Italy among users. Thus, such technologies must be designed
*
Corresponding author. with deep consideration of the specific needs of the end
†
These authors contributed equally. users and be able to adapt dynamically to their changing
$ iamaro@unisa.it (I. Amaro); aauriemmacitarella@unisa.it status and needs. The integration of AI systems with hu-
(A. Auriemma Citarella); fdemarco@unisa.it (F. De Marco); man users creates a mutually beneficial relationship pro-
adellagreca@unisa.it (A. Della Greca); ldibiasi@unisa.it
moting the design of Symbiotic AI (SAI). In SAI, humans
(L. Di Biasi); francese@unisa.it (R. Francese); dorossi@unisa.it
(D. Rossi); tortora@unisa.it (G. Tortora); ctucci@unisa.it (C. Tucci) and AI work together synergistically, each leveraging the
0000-0002-6525-0217 (A. Auriemma Citarella); strengths of the other to achieve common goals. This col-
0000-0003-4285-9502 (F. De Marco); 0000-0002-4900-8666 laborative approach emphasizes trust, transparency, and
(A. Della Greca); 0000-0002-9583-6681 (L. Di Biasi); effective communication between humans and AI sys-
0000-0002-6929-0056 (R. Francese); 0009-0005-6139-6920 (D. Rossi);
tems. Symbiotic AI has the potential to enhance decision-
0000-0003-4765-8371 (G. Tortora)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License making, problem-solving, and productivity across var-
Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
ious domains while maintaining control and oversight AI methods, highlighting the importance of reliable and
over the decision-making process [2]. resilient models. There is a need for trustworthiness in
In this context, our projects represent significant establishing confidence in AI systems, specifically in the
progress in the development of innovative strategies for context of Digital Health. A transparent AI system helps
early disease diagnosis and intervention methods. to build confidence among users by highlighting the sig-
nificance of understanding AI decisions from the perspec-
tive of SAI systems. To address this issue, we employed
2. Research Fields the GRADient-weighted Class Activation Mapping (Grad-
CAM) algorithm to show how five Convolutional Neural
Our primary research focus is medical image analysis,
Networks (CNNs), AlexNet, GoogleNet, ShuffleNet, Mo-
emphasizing the development of AI-driven and XAI al-
bileNetV2, and SqueezeNet, perform in identifying the
gorithms to understand their decisions. We aim to im-
most relevant features in images for the final decision of
prove diagnoses and outcomes across cardiovascular, skin
the model [5]. Table 1 highlights that, overall, the best
cancer (melanoma), Alzheimer’s (AD), and Parkinson’s
performance is obtained by AlexNet, which reached an
diseases (PD). Our research focuses on utilizing XAI tech-
82%, 87.4%, 75.8%, 81.5%, and 84.3% of Accuracy (ACC),
niques to establish a trustability index for AI models ana-
Sensitivity (SN), Specificity (SP), Precision (PRE) and F1
lyzing melanoma images and PVC signals. Through SAI
SCORE (F1), respectively. In Figure 1, the behavior of
systems, we aim to create reliable Computer-Aided Di-
networks on the same images demonstrates how distinct
agnosis (CAD) systems. Additionally, we employ neuro-
networks learn vastly different features when the Grad-
genetic Recurrent Neural Networks (RNNs) to identify
CAM algorithm is applied. The results highlighted the
PVCs and Deep Learning (DL) to classify COVID-19 in-
necessity for consistency and robustness in AI models
dividuals using Electrocardiogram (ECG) signals. Our
and allowed us to define a trustability index, defined as
research group designs and implements innovative AI
(Λ), in the range (Λ ∈ [0, 1]), where a value near 1
methods, focusing on mental health. Our proposed re-
indicates max trustability. We calculate the trustability
search aims to enhance the diagnosis of schizophrenic
index across all pairs and globally for all of the networks.
syndrome [3] and rehabilitation of neurodegenerative dis-
Results, validated by Λ calculation, indicate that relying
orders like Alzheimer’s [4]. Our goal is to build decision
on only one AI model for melanoma detection lacks reli-
support tools for mental health professionals’ clinical di-
ability without common shared patterns. It underscores
agnoses to reduce health care expenditures and promote
the importance of collaborative approaches, considering
early diagnosis and intervention. Additionally, we are
network interactions and synergies.
creating AI-based cognitive and emotional rehabilitation
solutions for AD patients to improve their quality of life
and reduce cognitive impairment through individualized Table 1
systems that take into account their unique traits. Performance results of the five CNNs
These activities were developed at the University of CNN ACC (%) SN (%) SP (%) PRE(%) F1 (%)
Salerno, particularly at the CAIS Lab1 . Alexnet 82 87.4 75.8 81.5 84.3
GoogleNet 75 91.9 54.9 71.3 80.3
ShuffleNet 76 89.2 59.3 72.8 80.2
3. Skin Cancer MobileNetV2
SqueezeNet
76
85
82
92.8
68.1
67
75.8
75.8
78.8
78.8
Melanoma is one of the most common types of skin can-
cer worldwide. In 2023, the American Cancer Society
recorded 97,610 new cases, affecting 58,120 men and
39,490 women 2 . Despite representing just 1% of skin
cancers, melanoma sustains a high mortality rate, under-
scoring the essential need for early diagnosis enabled by
CAD systems.
3.1. XAI on melanoma images
One of the crucial aspects that makes building reliable Figure 1: Two different networks learn very different features
CAD systems difficult is the fact that differences in neu- from Benign and malignant skin lesions (1 lesion at row).
ral network structures might undermine confidence in
1
https://caislab.di.unisa.it
2
https://www.cancer.org/cancer/melanoma-skin-
cancer/about/key-statistics.html
Table 2 for avoiding misdiagnosis. We started from two main
Final Results hypotheses: first, different types of PVCs correlate with
Models ACC RE SP PRE F1 gmean AUC distinct patterns in the electrical signal; second if various
ResNet-18 98,9% 95,3% 100% 100% 97,6% 97,6% 99,4% PVC types exist, it is feasible to divide an ECG dataset
ResNet-50
SqueezeNet
98,5%
98,8%
96,9%
95,8%
99%
99,7%
96,7%
99,1%
96,8%
97,4%
97,9%
97,7%
99,4%
99,4%
into clusters, each associated with a potential outcome.
MobileNetV2 87,7% 93,5% 86,7% 55,5% 69,7% 90,1% 98,3% In this work [6], we investigated the potential of the Ex-
AlexNet 86% 94% 84,6% 51,9% 66,9% 89,2% 98,1%
OurCNN 75,2% 91,3% 72,4% 37% 52,6% 81,3% 97,5% tended Genetic Algorithms (EGA) approach to design
RNNs. In particular, Long Short-Term Memory (LSTM)
and bidirectional LSTM (BLSTM) models are employed
4. Cardiovascular Diseases to examine QRS complexes in the MIT-BIH Arrhythmia
Database, which includes both non-PVC and PVC data,
The prevalence of cardiovascular disease (CVD) is rapidly to understand the typical features of standard QRS sig-
increasing over the world. According to the World Health nals and learn to reproduce them. Genetic algorithms
Organization (WHO), CVD accounted for 17.9 million (GAs) are optimization techniques inspired by biological
deaths in 2019. This data underscores the global effects of principles of the evolutionary theory. The neuroevolu-
CVD and highlights the significance of preventive actions tion approach employs these evolutionary algorithms
to reduce its impact on individuals and society. to produce artificial neural networks (ANN), parame-
ters, and rules. They enable the discovery of optimal
4.1. SARS-CoV-2 detection through DL solutions to optimization problems by emulating natural
selection, iterating through generations to improve can-
The uncontrolled transmission of the COVID-19 pan- didate solutions. Consequently, these trained RNNs can
demic required the adoption of preventive measures such accurately assess new ECGs with normal QRS complexes
as surgical masks, hand sanitization, and social distanc- by minimizing Root mean square error (RMSE) between
ing. Although molecular swabs and X-rays are recog- predictions and actual values. These architectures allow
nized as the most accurate diagnostic methods, they have us to achieve a Root Mean Square Error (RMSE) of 15. Ini-
disadvantages in terms of time, expense, and invasiveness. tial results suggest that PVC patterns likely reside in the
Therefore, the significance of researching alternate meth- central segments of ECG signals (Figure 2). RSME values
ods, such as ECG signals, increased. The objective of this in these segments support this observation, emphasizing
study was to establish a correlation between ECGs and their importance for precise PVC detection.
COVID-19 infection in a dataset of 1937 images divided
into: patients with COVID-19 (COVID), with Myocar-
dial Infarction (MI), with Previous History of Myocar-
dial Infarction (HMI), Abnormal Heartbeat (ABN) and
healthy (N). The study applied different Deep Learning
algorithms, such as MobileNetV2, ResNet-18, ResNet-50,
AlexNet, SqueezeNet, and a proprietary neural network.
Training sessions progressed through comparisons of
all five classes, then four (COVID-19, N, ABN, HMI),
followed by three (COVID-19, N, ABN), and finally in a
binary comparison (COVID-19 vs. N). The results (see Ta- Figure 2: Estimated ECG signal zones related to PVC with
ble 2) demonstrated a high level of accuracy in classifica- higher RMSE variability compared to normal QRS complexes.
tion, with ResNet-18 having an approximate accuracy of
98.94%, both for multiclass and binary classification. The
study suggests a correlation between ECG signals and
COVID-19 infection, potentially enabling the integration 4.3. CardioView: A XAI framework for
of proposed neural networks into healthcare monitoring detecting PVCs
systems for real-time detection.
In this study, we proposed a visual framework, called
CardioView, that integrates a human-in-the-loop (HITL)
4.2. Design of RNN with neuroevolution approach, ensuring continuous engagement and over-
approach sight of cardiologists during the PVC detection process.
In this initial phase of the work, our focus was on PVC
PVCs disrupt normal heart rhythms, particularly the QRS classification, the implementation of XAI algorithms, and
complex but their identification in ECG signals is crucial their clustering. Figure 3 outlines the workflow of the
proposed framework, beginning with pre-processing on
2
https://www.who.int/health-topics/cardiovascular-diseases
5. Neurological Diseases
Alzheimer’s disease (AD) is a condition characterized by
the gradual accumulation of abnormal proteins, particu-
larly amyloid- (A) and hyperphosphorylated tau, within
the brain. This accumulation causes progressive impair-
ment of synaptic function, neuronal health, and axonal
integrity [7]. Regarding cognitive features, AD mainly af-
Figure 3: CardioView workflow fects episodic memory (EM), semantic memory (SM), and
spatial abilities (SA). However, the clinical presentation
may vary in moderate or early cases. AD patients not
the MIT-BIH Arrhythmia Database to enhance data qual- only experience memory and visuospatial disorders but
ity. It involves two phases: training a CNN model on a also a range of symptoms that affect their emotional well-
black-and-white (BW) dataset using classic and k-fold being. These symptoms include difficulties in problem-
methods, followed by feature extraction. The RGB (red, solving, feelings of sadness, and lack of motivation, which
green, and blue) dataset is then trained using the classic substantially impact the patient’s daily functioning.
method, with the resulting model applied to the Grad-
CAM algorithm for dataset modification and retraining. 5.1. Technology-driven rehabilitation
Trained models extracted vital features, employed in k-
means and Density-Based Spatial Clustering of Appli-
strategies
cations with Noise (DBSCAN) clustering algorithms to In recent years, due to advances in AI, research has turned
identify patterns of PVC and non-PVC for early diag- increasing attention to the analysis and design of reha-
nosis. Grad-CAM facilitated the visualization of ECG bilitative interventions targeting AD patients based on
waveform segments, effectively distinguishing between the use of advanced technologies such as computerized
PVC and non-PVC, highlighting potential multiple PVC cognitive training (CCT) and interventions that exploit
classes, indicating diverse outcomes. The proposed CNN social robotics.
achieved 96.21% of ACC, 98.09% of recall (RE), 94.74% of These approaches are aimed at providing adequate ther-
PRE, and 99.28% of Area under the ROC Curve (AUC) apeutic support to counter the decline of cognitive func-
on the GRADCAM dataset, highlighting promising re- tion in AD patients, presenting themselves as comple-
sults in PVC detection. CardioView integrates cardiolo- mentary or alternative options to traditional therapies.
gist surveys, crucial for the human-in-the-loop process The connection between sophisticated technological
integral to reinforcement learning (RL). This approach tools and targeted rehabilitation methodologies is a grow-
emphasizes the active participation of cardiologists, en- ing area of research in digital health and, in particular, in
hancing the reliability and accuracy of PVC detection. caring for the elderly with neurodegenerative diseases.
CardioView is structured to collect data via surveys dis-
tributed to cardiologists, assisting in algorithm refine- 5.2. Mosaic of Memory: a serious game
ment. The dynamic survey involving experienced car-
diologists to express confidence levels in the AI model for Alzheimer’s patients
outcomes (see Figure 4). Cardiologists evaluate 20 im-
ages, providing feedback on XAI efficacy and suggesting
improvements if needed, promoting iterative refinement
in PVC detection.
Figure 5: MOM experimental flow
Figure 4: Step of the survey in CardioView Mosaic of Memory (MoM) is a serious game designed
for AD patients inspired by the traditional "memory" card
game. The game’s objective is to make correct matches
of cards presented on a virtual grid, depicting the faces
of the patient’s loved ones. MoM falls within the scope
of CCTs and aims to slow down the deterioration of (i)
spatial memory, as it is necessary to memorize the spa-
tial positions of cards to proceed with the game, and (ii)
autobiographical memory, due to the personalized con-
tent of the game. MoM leverages a multimodal approach
that combines visual and auditory stimuli to achieve its
therapeutic goals and facilitate memory reinforcement.
While the cards convey visual stimuli, the auditory stim-
uli consist of audio recordings of the people depicted on
the cards. This feature gives the game a high level of
customization, reinforced by MoM’s ability to dynami-
cally adapt to the user’s cognitive abilities through dif-
ferent game difficulty levels (easy, medium, expert) or
by selecting the automatic mode, which automatically
adapts the game to patient capacity. This ensures that the
game is always challenging but not frustrating for the
player. When players experience high frustration levels,
this negatively influences the gaming experience, leading
to anger and stress. For this reason, MoM is equipped
with AI, particularly an AU R-CNN model, designed to
detect facial microexpressions through a multi-label clas-
sification approach [8]. This improves MoM’s ability to
detect and respond to player frustration during game-
play. An additional distinguishing feature of the appli- Figure 6: RetroMind Framework
cation is the dedicated patient profile for gaming and
an administrative interface explicitly aimed at a loved
one or the AD patient’s therapist. This interface allows
their cognitive functioning and emotional well-being.
you to customize the user experience by changing the
2) LLM customisation: Healthcare professionals per-
background color and cards and modifying the game
sonalise ChatGPT 3.5 Turbo based on the information
content (images, names, and audio recordings of loved
collected in phase one. enabling ChatGPT 3.5 Turbo to
ones). The system also records detailed data from each
personalise patient interaction.
session, including games played and completed, average
3) Interaction phase: The robot Pepper, adapted to the
frustration rate, aids used, average play times, and num-
needs of the patient, supports the therapist in administer-
ber of games played by difficulty. These features allow
ing the AMI test. The robot captures the patient’s speech,
healthcare providers to monitor the patient’s progress
converts it into text and sends it to ChatGPT 3.5 Turbo.
and tailor the gaming experience to her needs.
Simultaneously, the content of the patient’s speech is
transformed into visual representations by DALL-E3, pro-
5.3. RetroMind: a support tool for viding an image as output.
reminiscence therapy. 4) Interaction and image phase: Pepper presents the
patient with the images generated by DALL-E3 in the
RetroMind framework is preliminary study which com-
previous phase. With the support of the image, the pa-
bines Large Language Models (LLMs) and social robots
tient’s narration is stimulated, reinforcing the memory
to enhance reminiscence therapy for Alzheimer’s disease
of the events by arousing positive emotions.
patients. Aims to support mental health professionals by
5) CSDD Administration: In the final phase, the men-
providing visual representations of patients’ life memo-
tal health professional administers the CSDD to monitor
ries, facilitating personalized and empathic interactions
changes in the patient’s emotional state compared to
tailored for each of them.
baseline levels, ensuring an ongoing assessment.
The RetroMind framework procedure consists of five
RetroMind is an innovative system that aims to improve
steps as shown in figure 6:
the effectiveness of reminiscence therapy. It acts as a
1) Traditional Therapy: Mental health profession-
narrative support tool using generated images, while
als administer the Autobiographical Memory Interview
continuously monitoring the patient’s cognitive perfor-
(AMI)[9] and the Cornell Scale for Depression in Demen-
mance and emotional state. What distinguishes Retro-
tia (CSDD)[10] tests to Alzheimer’s disease patients, col-
Mind from other existing solutions in the literature is its
lecting and transcribing patient responses to understand
unique ability to recreate representations of memories References
for individuals who may not have access to actual images
of their past. [1] C. Trocin, P. Mikalef, Z. Papamitsiou, K. Conboy,
Responsible ai for digital health: a synthesis and a
research agenda, Information Systems Frontiers 25
5.4. Dynamic Visualization of Gene (2023) 2139–2157.
Ontology Terms in AD and PD [2] B. Mahmud, G. Hong, B. Fong, A study of human–ai
In the domain of systems biology, where intricate net- symbiosis for creative work: Recent developments
works of biological molecules interact to regulate the and future directions in deep learning, ACM Trans-
processes of an organism, the use of visual and in- actions on Multimedia Computing, Communica-
teractive data representations is a critical aspect to tions and Applications 20 (2023) 1–21.
aid in intuitively communicating complex knowledge. [3] I. Amaro, R. Francese, G. Tortora, C. Tucci,
This is particularly true when navigating through vast L. D’Errico, M. Staffa, Supporting schizophrenia pa-
multilevel data sets that encompass various omics sci- tients’ care with robotics and artificial intelligence,
ences such as genomics, transcriptomics, proteomics, in: International Conference on Human-Computer
and metabolomics. This study introduced a human- Interaction, Springer, 2023, pp. 482–495.
interaction system for visualizing similarity data based [4] C. Tucci, I. Amaro, A. Della Greca, G. Tortora, Retro-
on Gene Ontology (GO) functions (Cellular Component mind and the image of memories: a preliminary
-CC, Molecular Function -MF, and Biological Process- BP) study of a support tool for reminiscence therapy,
related to AD and PD proteins/genes [11]. Similarity in: Lecture Notes in Computer Science, Springer,
data was generated using Lin and Wang distance mea- 2024.
sures across all three areas of GO. The data was then [5] A. Auriemma Citarella, F. De Marco, L. Di Biasi, ,
clustered using the K-means algorithm, and a dynamic, G. Tortora, Trustability is all you need! symbiotic
interactive view was developed using SigmaJS to allow ai strategies for melanoma diagnosis, in: ECCV,
users to customize the analysis workflow interactively. 2024.
To deepen our understanding of the functional relation- [6] F. De Marco, L. Di Biasi, A. Auriemma Citarella,
ships between GO terms, we introduced a spatial distance G. Tortora, Improving pvc detection in ecg signals:
metric, denoted as 𝑆𝐷, specifically utilized for visual- A recurrent neural network approach, in: Italian
ization purposes in the rendering routines (see Figure 7). Workshop on Artificial Life and Evolutionary Com-
This approach provides a more immediate visualization, putation, Springer, 2023, pp. 256–267.
enabling users to capture the most relevant information [7] D. S. Knopman, H. Amieva, R. C. Petersen, G. Chéte-
within the three vocabularies of GO. It facilitates an omic lat, D. M. Holtzman, B. T. Hyman, R. A. Nixon, D. T.
view and enables multilevel analysis with finer details, Jones, Alzheimer disease, Nature reviews Disease
compared to the traditional cluster view, enhancing un- primers 7 (2021) 33.
derstanding of end-user knowledge. [8] C. Ma, L. Chen, J. Yong, Au r-cnn: Encoding expert
prior knowledge into r-cnn for action unit detection,
neurocomputing 355 (2019) 35–47.
[9] M. D. Kopelman, B. Wilson, A. D. Baddeley, The
autobiographical memory interview: a new assess-
ment of autobiographical and personal semantic
memory in amnesic patients, Journal of clinical
and experimental neuropsychology 11 (1989) 724–
744.
Figure 7: The contextual menu and an use case [10] G. S. Alexopoulos, R. C. Abrams, R. C. Young, C. A.
Shamoian, Cornell scale for depression in dementia,
Biological psychiatry 23 (1988) 271–284.
[11] A. A. Citarella, F. De Marco, L. Di Biasi, M. Risi,
G. Tortora, Gene ontology terms visualization with
Acknowledgments dynamic distance-graph and similarity measures
These studies were carried out within the FAIR - Fu- (s)., in: DMSVIVA, 2021, pp. 85–91.
ture Artificial Intelligence Research and received funding
from the European Union Next-GenerationEU (PIANO
NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MIS-
SIONE 4 COMPONENTE 2, INVESTIMENTO 1.3 – D.D.
1555 11/10/2022, PE00000013).