Mickael Gette

Background/Objectives: Periodontal inflammation is a key feature of periodontal diseases, but traditional diagnostic methods are limited by invasiveness and radiation exposure. This systematic review aims to evaluate the potential of thermography and infrared spectroscopy for the in vivo detection of periodontal inflammation and to assess their reliability for clinical use. Methods: In accordance with PRISMA guidelines, an electronic search of the MEDLINE (PubMed) database was conducted to identify relevant studies published between 2000 and October 2025 that investigated these imaging modalities in periodontal inflammation diagnosis. Results: The search identified 310 records; after exclusions, 13 studies were included, comprising 7 thermography studies and 6 infrared spectroscopy studies, for a total of 712 patients. The included studies demonstrated the feasibility of thermography and infrared spectroscopy for detecting inflammatory changes in periodontal tissues in vivo. These non-invasive imaging techniques may help overcome the limitations of conventional clinical and radiographic diagnostic methods, particularly invasiveness and exposure to ionizing radiation. Conclusions: This field remains underexplored, and further studies are required to validate diagnostic performance, standardize methodologies, and determine their clinical applicability in routine periodontal practice.

Background: The COVID-19 crisis has strained world health care systems. This study aimed to develop an innovative prediction score using clinical and biological parameters (PREDICT score) to anticipate the need of intensive care of COVID-19 patients already hospitalized in standard medical units. Methods: PREDICT score was based on a training cohort and a validation cohort retrospectively recruited in 2020 in the Marseille University Hospital. Multivariate analyses were performed, including clinical, and biological parameters, comparing a baseline group composed of COVID-19 patients exclusively treated in standard medical units to COVID-19 patients that needed intensive care during their hospitalization. Results: Independent variables included in the PREDICT score were: age, Body Mass Index, Respiratory Rate, oxygen saturation, C-reactive protein, neutrophil–lymphocyte ratio and lactate dehydrogenase. The PREDICT score was able to correctly identify more than 83% of patients that needed intensive care after at least 1 day of standard medical hospitalization. Conclusions: The PREDICT score is a powerful tool for anticipating the intensive care need for COVID-19 patients already hospitalized in a standard medical unit. It shows limitations for patients who immediately need intensive care, but it draws attention to patients who have an important risk of needing intensive care after at least one day of hospitalization.