An app to classify a 5-year survival in patients with breast cancer using the convolutional neural networks (CNN) in Microsoft Excel: Development and usability study

Using chord diagrams to explore article themes in 100 top-cited articles citing Hirsch's h-index since 2005: A bibliometric analysis

BACKGROUND

The h-index is increasingly being used as a measure of individual research achievement (IRA). More than 4876 citing articles have been published and indexed in Web of Science. The articles citing the h-index that have made the greatest contribution to scientific academics are still unknown. It is also unclear which subject categories (SCs) can be classified based on their keywords.

METHODS

These 4976 citing articles have been collected from the Web of Science since 2005. SCs were classified using chord diagrams to visualize their associations of SCs and documents in 100 top-cited articles (T100hciting). In addition to chord diagrams, 6 visualizations were used to illustrate study results: choropleth maps were used to depict the geographical distribution of publications across countries, network diagrams were created by using coword analysis, box plots were created to complement the network diagrams, Sankey diagrams highlighted the 5 most important elements in each article entity, the dot plot was used for displaying T100hciting, and a radar plot was used to present the top 10 high-IRA elements of countries, institutes, departments, and authors based on category, journal impact factor, authorship, and L-index scores.

RESULTS

A coword cluster analysis indicates that the majority of articles come from the US (918, 18%) and China (603, 12%), the top 2 SCs are h-index and bibliometric analysis, and the top 5 countries account for 55% in T100hciting, such as the US (25%), Spain (10%), Netherlands (9%), China (6%), and Belgium (5%). In T100hciting, 4 SCs are included, namely, the h-index (72%), bibliometric analysis (24%), physics & multidisciplinary (3%), and infectious diseases (1%).

CONCLUSION

A total of 7 visualizations were used to display the results in this study. Chord diagrams are suggested as a tool for future bibliographical studies to classify SCs Future bibliometrics with chord diagrams should not be limited to the topic of h-index-citing articles, as we did in this study.

An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN): Comparison of prediction accuracy in Microsoft Excel

BACKGROUND

Dementia is a progressive disease that worsens over time as cognitive abilities deteriorate. Effective preventive interventions require early detection. However, there are no reports in the literature concerning apps that have been developed and designed to predict patient dementia classes (DCs). This study aimed to develop an app that could predict DC automatically and accurately for patients responding to the clinical dementia rating (CDR) instrument.

METHODS

A CDR was applied to 366 outpatients in a hospital in Taiwan, with assessments on 25 and 49 items endorsed by patients and family members, respectively. The 2 models of convolutional neural networks (CNN) and artificial neural networks (ANN) were applied to examine the prediction accuracy based on 5 classes (i.e., no cognitive decline, very mild, mild, moderate, and severe) in 4 scenarios, consisting of 74 (items) in total, 25 in patients, 49 in family, and a combination strategy to select the best in the aforementioned scenarios using the forest plot. Using CDR scores in patients and their families on both axes, patients were dispersed on a radar plot. An app was developed to predict patient DC.

RESULTS

We found that ANN had higher accuracy rates than CNN with a ratio of 3:1 in the 4 scenarios. The highest accuracy rate (=93.72%) was shown in the combination scenario of ANN. A significant difference was observed between the CNN and ANN in terms of the accuracy rate. An available ANN-based app for predicting DC in patients was successfully developed and demonstrated in this study.

CONCLUSION

On the basis of a combination strategy and a decision rule, a 74-item ANN model with 285 estimated parameters was developed and included. The development of an app that will assist clinicians in predicting DC in clinical settings is required in the near future.

Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer

Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians' treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.

Artificial intelligence for predicting five-year survival in stage IV metastatic breast cancer patients: A focus on sarcopenia and other host factors

We developed an artificial intelligence (AI) model that can predict five-year survival in patients with stage IV metastatic breast cancer, mainly based on host factors and sarcopenia. From a prospectively built breast cancer registry, a total of 210 metastatic breast cancer patients were selected in a consecutive manner using inclusion/exclusion criteria. The patients’ data were divided into two categories: a group that survived for more than 5 years and a group that did not survive for 5 years. For the AI model input, 11 features were considered, including age, body mass index, skeletal muscle area (SMA), height-relative SMA (H-SMI), height square-relative SMA (H2-SMA), weight-relative SMA (W-SMA), muscle mass, anticancer chemotherapy, radiation therapy, and comorbid diseases such as hypertension and mellitus. For the feature importance analysis, we compared classifiers using six different machine learning algorithms and found that extreme gradient boosting (XGBoost) provided the best accuracy. Subsequently, we performed the feature importance analysis based on XGBoost and proposed a 4-layer deep neural network, which considered the top 10 ranked features. Our proposed 4-layer deep neural network provided high sensitivity (75.00%), specificity (78.94%), accuracy (78.57%), balanced accuracy (76.97%), and an area under receiver operating characteristics of 0.90. We generated a web application for anyone to easily access and use this AI model to predict five-year survival. We expect this web application to be helpful for patients to understand the importance of host factors and sarcopenia and achieve survival gain.

Artificial intelligence empowered Digital Health Technologies in Cancer Survivorship Care: a scoping review

Objective

The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process.

Methods

Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review.

Results

43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors’ care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing.

Conclusions

While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.