Digital twins (DTHs) and virtual twins (VTHs) in healthcare represent emerging technologies towards precision medicine, providing opportunities for patient-centric healthcare. Our scoping review aimed to map the current DTH and VTH technologies in oncology, summarize their technical solutions, and assess their credibility. A systematic search was conducted in the main bibliographic databases, identifying 441 records, of which 30 were included. The studies covered a wide range of cancers, including breast, lung, colorectal, and gastrointestinal malignancies, with DTH and VTH applications focusing on diagnosis, therapy, and monitoring. The results revealed heterogeneity in targeted topics, technical approaches, and outcomes. Most twining solutions use synthetic or limited real-world data, raising concerns regarding their reliability. Few studies have integrated real-time data and machine learning for predictive modeling. Technical challenges include data integration, scalability, and ethical considerations, such as data privacy and security. Moreover, the evidence lacks sufficient clinical validation, with only partial credibility in most cases. Our findings underscore the need for multidisciplinary collaboration among end-users and developers to address the technical and ethical challenges of DTH and VTH systems. Although promising for the future of personalized oncology, substantial steps are required to move beyond experimental frameworks and to achieve clinical implementation.

Parkinson’s disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson’s disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.

The aim of this study is to evaluate salivary remineralisation versus chemical remineralisation/infiltration of enamel, using different dentistry materials. The enamel changes were studied using confocal laser scanning microscopy (CLSM), and the depth of lesions and demineralisation/remineralisation/infiltration percentage were calculated. Additionally, the macro elemental composition of the teeth was performed using atomic absorption spectroscopy (AAS). Two studies were performed: (i) demineralisation of enamel in 3% citric acid and infiltration treatment with infiltration resin (Icon, DMG), remineralisation with Fluor Protector (Ivoclar Vivadent) and artificial saliva pH 8; and (ii) enamel demineralisation in saliva at pH 3 and remineralisation at salivary pH 8. The results showed that, firstly, for the remineralisation of demineralised enamel samples, Fluor Protector (Ivoclar Vivadent) was very effective for medium demineralised lesions followed by saliva remineralisation. In cases of deep demineralisation lesions where fluoride could not penetrate, low viscosity resin (Icon, DMG, Hamburg) effectively infiltrated to stop the demineralisation process. Secondly, remineralisation in salivary conditions needed supplementary study over a longer period, to analyse the habits, diet and nutrition of patients in detail. Finally, demineralisation/remineralisation processes were found to influence the macro elemental composition of enamel demineralisation, with natural saliva proving to be less aggressive in terms of decreasing Ca and Mg content.

Background: Parkinson’s disease (PD) has transitioned from a rare condition in 1817 to the fastest-growing neurological disorder globally. The significant increase in cases from 2.5 million in 1990 to 6.1 million in 2016, coupled with predictions of a further doubling by 2040, underscores an impending healthcare challenge. This escalation aligns with global demographic shifts, including rising life expectancy and a growing global population. The economic impact, notably in the U.S., reached $51.9 billion in 2017, with projections suggesting a 46% increase by 2037, emphasizing the substantial socio-economic implications for both patients and caregivers. Coupled with a worldwide demand for health workers that is expected to rise to 80 million by 2030, we have fertile ground for a pandemic. Methods: Our transdisciplinary research focused on early PD detection through running speech and continuous handwriting analysis, incorporating medical, biomedical engineering, AI, and linguistic expertise. The cohort comprised 30 participants, including 20 PD patients at stages 1–4 on the Hoehn and Yahr scale and 10 healthy controls. We employed advanced AI techniques to analyze correlation plots generated from speech and handwriting features, aiming to identify prodromal PD biomarkers. Results: The study revealed distinct speech and handwriting patterns in PD patients compared to controls. Our ParkinsonNet model demonstrated high predictive accuracy, with F1 scores of 95.74% for speech and 96.72% for handwriting analyses. These findings highlight the potential of speech and handwriting as effective early biomarkers for PD. Conclusions: The integration of AI as a decision support system in analyzing speech and handwriting presents a promising approach for early PD detection. This methodology not only offers a novel diagnostic tool but also contributes to the broader understanding of PD’s early manifestations. Further research is required to validate these findings in larger, diverse cohorts and to integrate these tools into clinical practice for timely PD pre-diagnosis and management.

Purpose of the articleto carry out an epidemiological study on the use of sealing as a method of preventing dental caries and to assess the tightness and integrity over time of composite resins used to seal pits and fissures using the VistaCam iX intraoral fluorescence camera. Based on the premise that dentists do not have enough confidence in sealing pits and fissures as a method of preventing dental caries, we assessed if sealing is used in current practice as a prevention metod of tooth decay, and we measured, with high performance intraoral camera such as the VistaCam iX device, the tightness and integrity of composite resin seals.

As far as the problem of processing real time images studies showed that there can be two ways of analyze. On one hand is about the central processing unit (CPU) and the other is about the graphics processing unit (GPU). To obtain the highest possible performance they have to be used at the same time. Comparing the two processes, the result is that in the CPU, assignments as sequential operations and input-output are involved, in contrast to the GPU which is used for processes that can be made at the same time. Transposing these concepts of the real-time processing in medical digital images can be proved that the processing of an image is a response aproved within a certain time. The time we are speaking about lasts a few seconds or more less and images can be evaluated in a real-time medical side. Real-time image processing has become a very important objective and studied on the strength of its use in innovative media production, video calls, mobile phone cameras and video communication. The main purpose of this work is to improve the researching of a real-time digital image processing system applied on medical images. In this paper we try to present a method which is going to expose healthy and malignant tissues. Even if the physicians decisions remain the common method of detecting nonpalpable cancer, the method proposed in this paper plan to generate the fastest, easiest, and more accurate route, than in the case with classical techniques, by means of medical procedures. Our method of digital image processing by real-time processing is performed in order to allow medical specialists in radiology to detect abnormal tissues or cells, and then determine if a tumor is a malign or benign. The research illustrated in this paper involves an application toolbox with different filters, including segmentation, as well as various new tools for morphological filtering. More specifically, this paper focuses on CUDA as its parallel programming platform.