Objective To evaluate color, lightness, chroma, hue, and translucency adjustment potential of resin composites using CIEDE2000 color difference formula. Methods Three resin composites (Filtek Universal, Harmonize, and Omnichroma) were tested. Two types of specimens were prepared: an outer base shade with an inner hole filled with test shades and single‐composite specimens of all shades. Spectrorradiometric reflectances measurements and subsequent CIELAB color coordinates and translucency parameter (TP) were performed. Color (CAP00), lightness, chroma, hue, and translucency (TAP00) adjustment potential using CIEDE2000 color difference were computed. Color and transparency differences among composite materials and shades were statistically tested (P < 0.05). Results Positive CAP00 and TAP00 values were found for majority of tested materials. CAP00 values ranged from −0.14 to 0.89, with the highest values found for Omnichroma (>0.75 in all cases). TAP00 values ranged from −0.06 to 0.86 with significant translucency differences among dual and single specimens. Omnichroma exhibited the highest adjustment potential for all color dimensions studied. Conclusions Lightness, hue, chroma, and translucency adjustment potential have been introduced using CIEDE2000 color difference formula, and have shown their usefulness to evaluate blending effect in dentistry. Color coordinates and translucency adjustment potential were dependent on dental material. Omnichroma exhibited the most pronounced blending effect. Clinical significance Resin composites with increased color and translucency adjustment may simplify shade selection, making this process easier and less time consuming. Furthermore, these materials might facilitate challenging and complex color matching situations.

The mDurance® system is an innovative digital tool that combines wearable surface electromyography (sEMG), mobile computing and cloud analysis to streamline and automatize the assessment of muscle activity. The tool is particularly devised to support clinicians and sport professionals in their daily routines, as an assessment tool in the prevention, monitoring rehabilitation and training field. This study aimed at determining the validity of the mDurance system for measuring muscle activity by comparing sEMG output with a reference sEMG system, the Delsys® system. Fifteen participants were tested during isokinetic knee extensions at three different speeds (60, 180, and 300 deg/s), for two muscles (rectus femoris [RF] and vastus lateralis [VL]) and two different electrodes locations (proximal and distal placement). The maximum voluntary isometric contraction was carried out for the normalization of the signal, followed by dynamic isokinetic knee extensions for each speed. The sEMG output for both systems was obtained from the raw sEMG signal following mDurance's processing and filtering. Mean, median, first quartile, third quartile and 90th percentile was calculated from the sEMG amplitude signals for each system. The results show an almost perfect ICC relationship for the VL (ICC &gt; 0.81) and substantial to almost perfect for the RF (ICC &gt; 0.762) for all variables and speeds. The Bland-Altman plots revealed heteroscedasticity of error for mean, quartile 3 and 90th percentile (60 and 300 deg/s) for RF and at mean and 90th percentile for VL (300 deg/s). In conclusion, the results indicate that the mDurance® sEMG system is a valid tool to measure muscle activity during dynamic contractions over a range of speeds. This innovative system provides more time for clinicians (e.g., interpretation patients' pathologies) and sport trainers (e.g., advising athletes), thanks to automatic processing and filtering of the raw sEMG signal and generation of muscle activity reports in real-time.

Objective To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research. Materials and methods The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years. Results Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling. Conclusions The insight provided by the present work has reported outstanding results in the design of high‐performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations. Clinical significance The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

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