Search results

Accurate diagnosis of brain tumors is crucial for effective treatment and improved patient outcomes. Magnetic resonance imaging (MRI) is a common method for detecting brain malignancies, but interpreting MRI data can be challenging and time-consuming for healthcare professionals.

An innovative method is presented that combines deep learning (DL) models with natural language processing (NLP) from ChatGPT to enhance the accuracy of brain tumor detection in MRI scans. The method generates textual descriptions of brain tumor regions, providing clinicians with valuable insights into tumor characteristics for informed decision-making and personalized treatment planning.

The evaluation of this approach demonstrates promising outcomes, achieving a notable Dice coefficient score of 0.93 for tumor segmentation, outperforming current state-of-the-art methods. Human validation of the generated descriptions confirms their precision and conciseness.

While the method showcased advancements in accuracy and understandability, ongoing research is essential for refining the model and addressing limitations in segmenting smaller or atypical tumors.

These results emphasized the potential of this innovative method in advancing neuroimaging practices and contributing to the effective detection and management of brain tumors.

This study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.

Multiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.

The study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.

This study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

3D-printed devices proved their efficacy across different clinical applications, helping personalize medical treatments. This paper aims to present the procedure for the design and production of patient-specific dynamic simulators of the human knee. The scope of these simulators is to improve surgical outcomes, investigate the motion and load response of the human knee and standardize in-vitro experiments for testing orthopedic devices through a personalized physical representation of the patient’s joint.

This paper tested the approach on three volunteers. For each, a patient-specific mathematical joint model was defined from an magnetic resonance imaging (MRI) of the knee. The model guided the CAD design of the simulators, which was then realized through stereolithography printing. Manufacturing accuracy was tested by quantifying the differences between 3D-printed and CAD geometry. To assess the simulator functionality, its motion was measured through a stereophotogrammetric system and compared with the natural tibio-femoral motion of the volunteers, measured as a sequence of static MRI.

The 3D-printing accuracy was very high, with average differences between ideal and printed parts below ± 0.1 mm. However, the assembly of different 3D-printed parts resulted in a higher average error of 0.97 mm and peak values of 2.33 mm. Despite that, the rotational and translational accuracy of the simulator was about 5° and 4 mm, respectively.

Although improvements in the production process are needed, the proposed simulators successfully replicated the individual articular behavior. The proposed approach is general and thus extendible to other articulations.

This paper aims to critically evaluate the role of advanced artificial intelligence (AI)-enhanced image fusion techniques in lung cancer diagnostics within the context of AI-driven precision medicine.

We conducted a systematic review of various studies to assess the impact of AI-based methodologies on the accuracy and efficiency of lung cancer diagnosis. The focus was on the integration of AI in image fusion techniques and their application in personalized treatment strategies.

The review reveals significant improvements in diagnostic precision, a crucial aspect of the evolution of AI in healthcare. These AI-driven techniques substantially enhance the accuracy of lung cancer diagnosis, thereby influencing personalized treatment approaches. The study also explores the broader implications of these methodologies on healthcare resource allocation, policy formation, and epidemiological trends.

This study is notable for both emphasizing the clinical importance of AI-integrated image fusion in lung cancer treatment and illuminating the profound influence these technologies have in the future AI-driven healthcare systems.

This case study involves interviews with radiologists of various hospitals and with company personnel. Both primary and secondary data sources have been used. The first-hand perspective from the radiologists highlighted the challenges they face concerning time and the patient load. The company personnel highlighted using machine learning for used cases to make the platform more robust and accurate. This case has been tested with MBA students.

An emerging health-care artificial intelligence (AI) start-up, DeepTek.AI, wants to expand its reach in the radiology market. The company intends to leverage technology to assist radiologists in diagnostics. India's health-care sector faces the challenge of needing more trained doctors and nurses to meet the ever-increasing needs of patients. This case study revolves around the radiologists' concerns about implementing the new technology and its ease of use. The features and benefits of integrating AI in diagnostics are the need of the hour, but the reliability of results needs to be ascertained for adopting it.

This case was written for marketing applications and practices, trends in marketing, marketing strategy and technology adoption in marketing courses at the post-graduate level. Consumer adoption of finance, hospitality, travel and health-care technology is vital for increasing the company's market share and growth prospects. The students will have an opportunity to understand the challenges and the opportunities.

There is an emerging interest in understanding the cognitive, emotional and motivational processes that drive tourists' behaviour using neuroscientific research methods. This chapter briefly reviews the main methods of interest to tourism researchers, to then focuses on electroencephalography, which reflects electrical activity from the brain. Event-related potentials or electroencephalography oscillations reflect cognitive and affective processes. Components of the former can index emotional brain responses, and alpha oscillations are related to attention and approach/withdrawal. Existing tourism literature/using electroencephalography are reviewed. This is a promising tool for studying a range of phenomena that are of interest to tourism scholars, but require careful use of methods and interpretation.

This study aims to evaluate the enhancement in prosthetic supply chain capabilities resulting from the implementation of additive manufacturing (AM) technologies. The study presents an emerging model outlining the key areas that undergo changes when integrating 3D printing technologies into the prosthetic supply chain.

Employing a qualitative approach, data were collected through field observations and 31 in-depth interviews conducted within various Jordanian organizations associated with the prosthetic industry and 3D printing technologies.

The findings suggest that the adoption of 3D printing technologies improves the prosthetic supply chain’s capabilities in terms of customization, responsiveness, innovation, environmental sustainability, cost minimization and patient empowerment. The study sheds light on the specific areas affected in the prosthetic supply chain following the adoption of 3D printing technologies, emphasizing the overall improvement in supply chain capabilities within the prosthetic industry.

This study provides recommendations for governmental bodies and prosthetic organizations to maximize the benefits derived from the use of 3D printing technologies.

This study contributes as the first of its kind in exploring the impact of 3D printing technology adoption in the Jordanian prosthetic industry, elucidating the effects on the supply chain and identifying challenges for decision-makers in an emerging market context.

The aim was not to perform a systematic review but firstly to search in PubMed, Science Direct, Scopus and Web of Science databases on the papers published in the last five years using tools for reviewing the statement of preferred information item for systematic reviews without focusing on a randomized analysis and secondly to perform a bibliometric analysis on the properties of films and coatings added of tocopherol for food packaging.

On January 24, 2022, information was sought on the properties of films and coatings added of tocopherol for use as food packaging published in PubMed, Science Direct, Scopus and Web of Science databases. Further analysis was performed using bibliometric indicators with the VOSviewer tool.

The searches returned 33 studies concerning the properties of films and coatings added of tocopherol for food packaging, which were analyzed together for a better understanding of the results. Data analysis using the VOSviewer tool allowed a better visualization and exploration of these words and the development of maps that showed the main links between the publications.

In the area of food science and technology, the development of polymers capable of promoting the extension of the shelf life of food products is sought, so the knowledge of the properties is vital for this research area since combining a biodegradable polymeric material with a natural antioxidant active is of great interest for modern society since they associate environmental preservation with food preservation.