AI Applied to Volatile Organic Compound (VOC) Profiles from Exhaled Breath Air for Early Detection of Lung Cancer.

Volatile organic compounds (VOCs) are an increasingly meaningful method for the early detection of various types of cancers, including lung cancer, through non-invasive methods. Traditional cancer detection techniques such as biopsies, imaging, and blood tests, though effective, often involve invasive procedures or are costly, time consuming, and painful. Recent advancements in technology have led to the exploration of VOC detection as a promising non-invasive and comfortable alternative. VOCs are organic chemicals that have a high vapor pressure at room temperature, making them readily detectable in breath, urine, and skin. The present study leverages artificial intelligence (AI) and machine learning algorithms to enhance classification accuracy and efficiency in detecting lung cancer through VOC analysis collected from exhaled breath air. Unlike other studies that primarily focus on identifying specific compounds, this study takes an agnostic approach, maximizing detection efficiency over the identification of specific compounds focusing on the overall compositional profiles and their differences across groups of patients. The results reported hereby uphold the potential of AI-driven techniques in revolutionizing early cancer detection methodologies towards their implementation in a clinical setting.

A prospective observational study for a Federated Artificial Intelligence solution for moniToring mental Health status after cancer treatment (FAITH): study protocol.

BACKGROUND: Depression is a common condition among cancer patients, across several points in the disease trajectory. Although presenting higher prevalence rates than the general population, it is often not reported or remains unnoticed. Moreover, somatic symptoms of depression are common in the oncological context and should not be dismissed as a general symptom of cancer. It becomes even more challenging to track psychological distress in the period after the treatment, where connection with the healthcare system typically becomes sporadic. The main goal of the FAITH project is to remotely identify and predict depressive symptoms in cancer survivors, based on a federated machine learning (ML) approach, towards optimization of privacy. METHODS: FAITH will remotely analyse depression markers, predicting their negative trends. These markers will be treated in distinct categories, namely nutrition, sleep, activity and voice, assessed in part through wearable technologies. The study will include 300 patients who have had a previous diagnosis of breast or lung cancer and will be recruited 1 to 5 years after the end of primary cancer. The study will be organized as a 12-month longitudinal prospective observational cohort study, with monthly assessments to evaluate depression symptoms and quality of life among cancer survivors. The primary endpoint is the severity of depressive symptoms as measured by the Hamilton Depression Rating Scale (Ham-D) at months 3, 6, 9 and 12. Secondary outcomes include self-reported anxiety and depression symptoms (HADS scale), and perceived quality of life (EORTC questionnaires), at baseline and monthly. Based on the predictive models gathered during the study, FAITH will also aim at further developing a conceptual federated learning framework, enabling to build machine learning models for the prediction and monitoring of depression without direct access to user's personal data. DISCUSSION: Improvements in the objectivity of psychiatric assessment are necessary. Wearable technologies can provide potential indicators of depression and anxiety and be used for biofeedback. If the FAITH application is effective, it will provide healthcare systems with a novel and innovative method to screen depressive symptoms in oncological settings. TRIAL REGISTRATION: Trial ID: ISRCTN10423782 . Date registered: 21/03/2022.

Review of Technology-Supported Multimodal Solutions for People with Dementia.

The number of people living with dementia in the world is rising at an unprecedented rate, and no country will be spared. Furthermore, neither decisive treatment nor effective medicines have yet become effective. One potential alternative to this emerging challenge is utilizing supportive technologies and services that not only assist people with dementia to do their daily activities safely and independently, but also reduce the overwhelming pressure on their caregivers. Thus, for this study, a systematic literature review is conducted in an attempt to gain an overview of the latest findings in this field of study and to address some commercially available supportive technologies and services that have potential application for people living with dementia. To this end, 30 potential supportive technologies and 15 active supportive services are identified from the literature and related websites. The technologies and services are classified into different classes and subclasses (according to their functionalities, capabilities, and features) aiming to facilitate their understanding and evaluation. The results of this work are aimed as a base for designing, integrating, developing, adapting, and customizing potential multimodal solutions for the specific needs of vulnerable people of our societies, such as those who suffer from different degrees of dementia.

Protocol for the Implementation and Assessment of "MoodUP": A Stepped Care Model Assisted by a Digital Platform to Accelerate Access to Mental Health Care for Cancer Patients Amid the COVID-19 Pandemic.

The COVID-19 pandemic has important consequences for the mental health of populations. Patients with cancer, already at risk for poor mental health outcomes, are not expected to be spared from these consequences, prompting the need for health services to improve responsiveness. This article presents the research protocol for an implementation study designed to describe the uptake of a well-studied and recognized system for the treatment of depression and anxiety (Stepped-care) during the specific context of a Pandemic in an oncological site. The system set-up will be assisted by a digital platform (MoodUP), where patients undergoing cancer treatment will be screened for anxiety and depressive symptoms, triaged by severity level and algorithm-matched to recommended interventions. Patients undergoing cancer treatment at a cancer clinic in Portugal will be invited to subscribe to the MoodUP platform where they will complete a self-reported questionnaire (Hospital Anxiety and Depression Scale) to screen their anxiety and depressive symptoms. Data will be instantly collected, and an algorithm will activate severity-matched intervention suggestions, through a case manager that will coordinate care. The specific objectives of this study will be to describe the implementation and acceptability of the care system by patients and staff, the barriers to and facilitators of implementation, the proportion of patients accessing the system and their pathways through the various stepped-care interventions, and patient perceptions regarding the feasibility and appropriateness of the eHealth platform. Moreover, exploratory analyses will be conducted to describe patterns of anxiety and depression symptoms variation across all patients, as well as within sociodemographically, clinically and contextually characterized subgroups, to characterize their care needs and access, as well as to explore for whom the MoodUP care system may be more appropriate. This study is expected to improve processes for collaborative mental healthcare in oncology and accelerate the digitalization of services, towards the improvement of mental healthcare access, and management of high-risk patients, during the COVID-19 pandemic.