Real-time virtual sonography in gynecology & obstetrics. literature's analysis and case series

Fusion Imaging is a latest generation diagnostic technique, designed to combine ultrasonography with a second-tier technique such as magnetic resonance imaging and computer tomography. It has been mainly used until now in urology and hepatology. Concerning gynecology and obstetrics, the studies mostly focus on the diagnosis of prenatal disease, benign pathology and cervical cancer. We provided a systematic review of the literature with the latest publications regarding the role of Fusion technology in gynecological and obstetrics fields and we also described a case series of six emblematic patients enrolled from Gynecology Department of Sant ‘Andrea Hospital, “la Sapienza”, Rome, evaluated with Esaote Virtual Navigator equipment. We consider that Fusion Imaging could add values at the diagnosis of various gynecological and obstetrics conditions, but further studies are needed to better define and improve the role of this fascinating diagnostic tool

Quantitative Analysis of Radiation-Associated Parenchymal Lung Change

Radiation-induced lung damage (RILD) is a common consequence of thoracic radiotherapy (RT). We present here a novel classification of the parenchymal features of RILD. We developed a deep learning algorithm (DLA) to automate the delineation of 5 classes of parenchymal texture of increasing density. 200 scans were used to train and validate the network and the remaining 30 scans were used as a hold-out test set. The DLA automatically labelled the data with Dice Scores of 0.98, 0.43, 0.26, 0.47 and 0.92 for the 5 respective classes. Qualitative evaluation showed that the automated labels were acceptable in over 80% of cases for all tissue classes, and achieved similar ratings to the manual labels. Lung registration was performed and the effect of radiation dose on each tissue class and correlation with respiratory outcomes was assessed. The change in volume of each tissue class over time generated by manual and automated segmentation was calculated. The 5 parenchymal classes showed distinct temporal patterns We quantified the volumetric change in textures after radiotherapy and correlate these with radiotherapy dose and respiratory outcomes. The effect of local dose on tissue class revealed a strong dose-dependent relationship We have developed a novel classification of parenchymal changes associated with RILD that show a convincing dose relationship. The tissue classes are related to both global and local dose metrics, and have a distinct evolution over time. Although less strong, there is a relationship between the radiological texture changes we can measure and respiratory outcomes, particularly the MRC score which directly represents a patient’s functional status. We have demonstrated the potential of using our approach to analyse and understand the morphological and functional evolution of RILD in greater detail than previously possible

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Target-specific multiphysics modeling for thermal medicine applications

Dissertation to obtain the degree of Doctor of Philosophy in Biomedical EngineeringThis thesis addresses thermal medicine applications on murine bladder hyperthermia and brain temperature monitoring. The two main objectives are interconnected by the key physics in thermal medicine: heat transfer. The first goal is to develop an analytical solution to characterize the heat transfer in a multi-layer perfused tissue. This analytical solution accounts for important thermoregulation mechanisms and is essential to understand the fundamentals underlying the physical and biological processes associated with heat transfer in living tissues. The second objective is the development of target-specific models that are too complex to be solved by analytical methods. Thus, the software for image segmentation and model simulation is based on numerical methods and is used to optimize non-invasive microwave antennas for specific targets. Two examples are explored using antennas in the passive mode (probe) and active mode (applicator). The passive antenna consists of a microwave radiometric sensor developed for rapid non-invasive feedback of critically important brain temperature. Its design parameters are optimized using a power-based algorithm. To demonstrate performance of the device, we build a realistic model of the human head with separate temperaturecontrolled brain and scalp regions. The sensor is able to track brain temperature with 0.4 °C accuracy in a 4.5 hour long experiment where brain temperature is varied in a 37 °C, 27 °C and 37 °C cycle. In the second study, a microwave applicator with an integrated cooling system is used to develop a new electro-thermo-fluid (multiphysics) model for murine bladder hyperthermia studies. The therapy procedure uses a temperature-based optimization algorithm to maintain the bladder at a desired therapeutic level while sparing remaining tissues from dangerous temperatures. This model shows that temperature dependent biological properties and the effects of anesthesia must be accounted to capture the absolute and transient temperature fields within murine tissues. The good agreement between simulation and experimental results demonstrates that this multiphysics model can be used to predict internal temperatures during murine hyperthermia studies

National eHealth system – platform for preventive, predictive and personalized diabetes care

National eHealth System, covering all citizens and all healthcare levels in Republic of Macedonia, was introduced in July 2013, has been internationally recognized System for successful reduction of waiting times and instrumental in the management of national healthcare resources. For the first time, National Diabetes Committee, formed in February 2015 according to the Law on healthcare and being overall responsible for the diabetes care in the country, was able to derive exact figures on the national diabetes prevalence from the System, instead of extrapolations used before, serving as a basis for development of strategies for prediction and prevention of diabetic complications, as well as for personalized diabetes care. Number of diabetes cases identified through the National eHealth System in June 2015 was 84,568 (4.02 % of total population), 36,119 males (3.42 % of total male population) and 48,449 females (4.61% of total female population). Age stratified diabetes prevalence was as follows: less than 20 years – 549 cases (0.11 % of respective population), 20-39 years – 3,202 (0.49 %), 40-59 years – 26,561 (4.58 %), 60-79 years – 48,470 (14.57 %), 80 years or more – 5,786 (12.96 %). Addition of parameters for metabolic control and diabetic complications in the System is under way, further facilitating the modeling of diabetes treatment, metabolic control and the outcomes. Inclusion of pre-diabetes patients (IGT and IFG) is also planned, thus providing opportunity to also focus healthcare activities for prevention of progression into overt type 2 diabetes

Faculty Publications and Creative Works 2004

Faculty Publications & Creative Works is an annual compendium of scholarly and creative activities of University of New Mexico faculty during the noted calendar year. Published by the Office of the Vice President for Research and Economic Development, it serves to illustrate the robust and active intellectual pursuits conducted by the faculty in support of teaching and research at UNM

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

Laser stimulated dynamic thermal imaging system for tumor detection

Laser stimulated dynamic thermal imaging system for tumor detectionby Hongyu Meng Doctor of Philosophy in Biomedical Engineering Washington University in St. Louis, 2021 Professor Samuel Achilefu, Chair Recent advances in infrared sensor technology have enabled the rapid application of thermal imaging in materials science, security and medicine. Relying on the infrared characteristics of living systems, thermal imaging has been used to generate individual heat maps, detect inflammation and tumor. As an imaging system, thermal imaging has the advantages of portability, real-time, non-invasive, and non-contact. But the low specificity of thermal imaging hinders its wide clinical application. Unfortunately, label-free DTI is less able to fully capture thermal tissue heterogeneity in high resolution due partly to how thermal stimulation is applied. Current DTI methods apply thermal stimulation to a large area of tissue, which obscures the detection of the unique thermal characteristics of a small area in the thermally disturbed area. Super-resolution DTI grating can improve the spatial resolution, but the system setup is complex. For biological samples, the use of exogenous contrast agents can enhance contrast, but contrast agents increase regulatory hurdles in clinical trials. In this work, we have developed a focused dynamic laser stimulation imaging (FDTI) system to overcome these limitations. The system, which has high resolution, high speed and large field of view uses a short wavelength laser to stimulate small tissue area and a thermal camera to acquire data. We captured thermal images and videos, extracted features, and built classifiers to distinguish tumors from normal tissues. Data analysis showed that FDTI method achieved high accuracy (classifier surpassed 90%) with spatial resolution attaining 1 mm, which surpasses conventional thermal imaging and DTI. We next explored the ability of FDTI to detect early-stage tumors by scanning multiple areas that exhibited normal thermal images with conventional thermal imaging. A bioluminescence imaging (BLI) system was then used to locate the tumor, which was co-registered to the FDTI images to determine the position of the laser spot. By extracting features from the collected thermal images and videos and constructing the classifier, the FDTI system achieved an accuracy greater than 80% in detecting early tumors in different mouse tumor models. Subsequently, the FDTI system was optimized to improve its acquisition speed, automation and robustness. First, we analyzed the influencing factors of imaging and proposed new system hardware designs to improve the data acquisition speed. Then, to shorten the acquisition time from the software level, we tested and analyzed the performance of features at different stages during the acquisition process. We also designed and tested registration markers, including registration results of different features, feature robustness under interference, marker detection from the background, and marker performance in motion correction to improve the degree of automation of the system. Furthermore, we tested the performance of thermal imaging applications in other research fields, including brain tumor detection, nerve damage assessment, and whether temperature changes correlate with stroke. These results show that FDTI is a promising technique for enhancing contrast, improving spatial resolution, determining underlying tumor heterogeneity, and detecting tumors at stages when conventional thermal imaging is ineffective. This work lays a strong foundation for diverse applications and clinical translation of FDTI to address unmet needs of current thermal imaging technologies

Effects of errorless learning on the acquisition of velopharyngeal movement control

Session 1pSC - Speech Communication: Cross-Linguistic Studies of Speech Sound Learning of the Languages of Hong Kong (Poster Session)The implicit motor learning literature suggests a benefit for learning if errors are minimized during practice. This study investigated whether the same principle holds for learning velopharyngeal movement control. Normal speaking participants learned to produce hypernasal speech in either an errorless learning condition (in which the possibility for errors was limited) or an errorful learning condition (in which the possibility for errors was not limited). Nasality level of the participants’ speech was measured by nasometer and reflected by nasalance scores (in %). Errorless learners practiced producing hypernasal speech with a threshold nasalance score of 10% at the beginning, which gradually increased to a threshold of 50% at the end. The same set of threshold targets were presented to errorful learners but in a reversed order. Errors were defined by the proportion of speech with a nasalance score below the threshold. The results showed that, relative to errorful learners, errorless learners displayed fewer errors (50.7% vs. 17.7%) and a higher mean nasalance score (31.3% vs. 46.7%) during the acquisition phase. Furthermore, errorless learners outperformed errorful learners in both retention and novel transfer tests. Acknowledgment: Supported by The University of Hong Kong Strategic Research Theme for Sciences of Learning © 2012 Acoustical Society of Americapublished_or_final_versio

Unveiling healthcare data archiving: Exploring the role of artificial intelligence in medical image analysis

Gli archivi sanitari digitali possono essere considerati dei moderni database progettati per immagazzinare e gestire ingenti quantità di informazioni mediche, dalle cartelle cliniche dei pazienti, a studi clinici fino alle immagini mediche e a dati genomici. I dati strutturati e non strutturati che compongono gli archivi sanitari sono oggetto di scrupolose e rigorose procedure di validazione per garantire accuratezza, affidabilità e standardizzazione a fini clinici e di ricerca. Nel contesto di un settore sanitario in continua e rapida evoluzione, l’intelligenza artificiale (IA) si propone come una forza trasformativa, capace di riformare gli archivi sanitari digitali migliorando la gestione, l’analisi e il recupero di vasti set di dati clinici, al fine di ottenere decisioni cliniche più informate e ripetibili, interventi tempestivi e risultati migliorati per i pazienti. Tra i diversi dati archiviati, la gestione e l’analisi delle immagini mediche in archivi digitali presentano numerose sfide dovute all’eterogeneità dei dati, alla variabilità della qualità delle immagini, nonché alla mancanza di annotazioni. L’impiego di soluzioni basate sull’IA può aiutare a risolvere efficacemente queste problematiche, migliorando l’accuratezza dell’analisi delle immagini, standardizzando la qualità dei dati e facilitando la generazione di annotazioni dettagliate. Questa tesi ha lo scopo di utilizzare algoritmi di IA per l’analisi di immagini mediche depositate in archivi sanitari digitali. Il presente lavoro propone di indagare varie tecniche di imaging medico, ognuna delle quali è caratterizzata da uno specifico dominio di applicazione e presenta quindi un insieme unico di sfide, requisiti e potenziali esiti. In particolare, in questo lavoro di tesi sarà oggetto di approfondimento l’assistenza diagnostica degli algoritmi di IA per tre diverse tecniche di imaging, in specifici scenari clinici: i) Immagini endoscopiche ottenute durante esami di laringoscopia; ciò include un’esplorazione approfondita di tecniche come la detection di keypoints per la stima della motilità delle corde vocali e la segmentazione di tumori del tratto aerodigestivo superiore; ii) Immagini di risonanza magnetica per la segmentazione dei dischi intervertebrali, per la diagnosi e il trattamento di malattie spinali, così come per lo svolgimento di interventi chirurgici guidati da immagini; iii) Immagini ecografiche in ambito reumatologico, per la valutazione della sindrome del tunnel carpale attraverso la segmentazione del nervo mediano. Le metodologie esposte in questo lavoro evidenziano l’efficacia degli algoritmi di IA nell’analizzare immagini mediche archiviate. I progressi metodologici ottenuti sottolineano il notevole potenziale dell’IA nel rivelare informazioni implicitamente presenti negli archivi sanitari digitali