Application of artificial intelligence in microbiome study promotes precision medicine for gastric cancer

Background: In the real world, medical practice is changing hand in hand with the development of new Artificial Intelligence (AI) systems and problems from different areas have been successfully solved using AI algorithms. Specifically, the use of AI techniques in setting up or building precision medicine is significant in terms of the accuracy of disease discovery and tailored treatment. Moreover, with the use of technology, clinical personnel can deliver a very much efficient healthcare service. Objective: This article reviews AI state-of-the-art in cardiovascular disease management, focusing on diagnostic and therapeutic improvements. Methods: To that end, we conducted a detailed PubMed search on AI application from distinct areas of cardiology: heart failure, arterial hypertension, atrial fibrillation, syncope and cardiovascular rehabilitation. Particularly, to assess the impact of these technologies in clinical decision-making, this research considers technical and medical aspects. Results: On one hand, some devices in heart failure, atrial fibrillation and cardiac rehabilitation represent an inexpensive, not invasive or not very invasive approach to long-term surveillance and management in these areas. On the other hand, the availability of large datasets (big data) is a useful tool to predict the development and outcome of many cardiovascular diseases. In summary, with this new guided therapy, the physician can supply prompt, individualised, and tailored treatment and the patients feel safe as they are continuously monitored, with a significant psychological effect. Conclusion: Soon, tailored patient care via telemonitoring can improve the clinical practice because AI-based systems support cardiologists in daily medical activities, improving disease detection and treatment. However, the physician-patient relationship remains a pivotal step.

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Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson’s Disease?

Journal of Parkinson s Disease ◽

10.3233/jpd-212545 ◽

2021 ◽

pp. 1-6

Author(s):

Matt Landers ◽

Suchi Saria ◽

Alberto J. Espay

Keyword(s):

Artificial Intelligence ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Precision Medicine ◽

Movement Disorders ◽

Living Environment ◽

Patient Status ◽

Patient Symptom ◽

Molecular Therapies ◽

Improve Patient

The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson’s disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.

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Will Artificial Intelligence Deliver Precision Medicine for Patients With Aortic Stenosis?

JACC Cardiovascular Interventions ◽

10.1016/j.jcin.2021.08.046 ◽

2021 ◽

Vol 14 (19) ◽

pp. 2141-2143

Author(s):

Nico Bruining ◽

Peter P.T. de Jaegere

Keyword(s):

Artificial Intelligence ◽

Aortic Stenosis ◽

Precision Medicine

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Modeling Research Topics for Artificial Intelligence Applications in Medicine: Latent Dirichlet Allocation Application Study

Journal of Medical Internet Research ◽

10.2196/15511 ◽

2019 ◽

Vol 21 (11) ◽

pp. e15511 ◽

Cited By ~ 3

Author(s):

Bach Xuan Tran ◽

Son Nghiem ◽

Oz Sahin ◽

Tuan Manh Vu ◽

Giang Hai Ha ◽

...

Keyword(s):

Artificial Intelligence ◽

Health Care ◽

Precision Medicine ◽

Data Science ◽

Latent Dirichlet Allocation ◽

Developed Countries ◽

Theory And Practice ◽

Clinical Practices ◽

Research Topics ◽

Dirichlet Allocation

Background Artificial intelligence (AI)–based technologies develop rapidly and have myriad applications in medicine and health care. However, there is a lack of comprehensive reporting on the productivity, workflow, topics, and research landscape of AI in this field. Objective This study aimed to evaluate the global development of scientific publications and constructed interdisciplinary research topics on the theory and practice of AI in medicine from 1977 to 2018. Methods We obtained bibliographic data and abstract contents of publications published between 1977 and 2018 from the Web of Science database. A total of 27,451 eligible articles were analyzed. Research topics were classified by latent Dirichlet allocation, and principal component analysis was used to identify the construct of the research landscape. Results The applications of AI have mainly impacted clinical settings (enhanced prognosis and diagnosis, robot-assisted surgery, and rehabilitation), data science and precision medicine (collecting individual data for precision medicine), and policy making (raising ethical and legal issues, especially regarding privacy and confidentiality of data). However, AI applications have not been commonly used in resource-poor settings due to the limit in infrastructure and human resources. Conclusions The application of AI in medicine has grown rapidly and focuses on three leading platforms: clinical practices, clinical material, and policies. AI might be one of the methods to narrow down the inequality in health care and medicine between developing and developed countries. Technology transfer and support from developed countries are essential measures for the advancement of AI application in health care in developing countries.

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Inverse correlation between Interleukin-34 and gastric cancer, a potential biomarker for prognosis

10.21203/rs.3.rs-26741/v1 ◽

2020 ◽

Author(s):

Qinghua Liu ◽

Ying Zhang ◽

Jiwei Zhang ◽

Kun Tao ◽

Brett D Hambly ◽

...

Keyword(s):

Gastric Cancer ◽

Prognostic Factor ◽

Early Diagnosis ◽

Precision Medicine ◽

Cellular Therapy ◽

Inverse Correlation ◽

Independent Prognostic Factor ◽

Cancer Tissue ◽

High Morbidity ◽

Potential Biomarker

Abstract Background Gastric cancer (GC) is a malignancy with high morbidity/mortality, partly due to a lack of reliable biomarkers for early diagnosis. It is important to develop reliable biomarker(s) with specificity, sensitivity and convenience for early diagnosis. The role of tumour-associated macrophages (TAMs) and survival of GC patients are controversial. Macrophage colony stimulating factor (MCSF) regulates monocytes/macrophages. Elevated MCSF is correlated with invasion, metastasis and poor survival of tumour patients. IL-34, a ligand of the MCSF receptor, acts as a “twin” to MCSF, demonstrating overlapping and complimentary actions. IL-34 involvement in tumours is controversial, possibly due to the levels of MCSF receptors. While the IL34/MCSF/MCSFR axis is very important for regulating macrophage differentiation, the specific interplay between these cytokines, macrophages and tumour development is unclear.Methods A multi-factorial evaluation could provide more objective utility, particularly for either prediction and/or prognosis of gastric cancer. Precision medicine requires molecular diagnosis to determine the specifically mutant function of tumours, and is becoming popular in the treatment of malignancy. Therefore, elucidating specific molecular signalling pathways in specific cancers facilitates the success of a precision medicine approach. Gastric cancer tissue arrays were generated from stomach samples with TNM stage, invasion depth and the demography of these patients (n = 185). Using immunohistochemistry/histopathology, MCSF, IL-34 and macrophages were determined.Results We found that IL-34 may serve as a predictive biomarker, but not as an independent, prognostic factor in GC; MCSF inversely correlated with survival of GC in TNM III‑IV subtypes. Increased CD68+TAMs were a good prognostic factor in some cases and could be used as an independent prognostic factor in male T3 stage GC.Conclusion Our data support the potency of IL-34, MCSF, TAMs and the combination of IL34/TAMs as novel biological markers for GC, and may provide new insight for both diagnosis and cellular therapy of GC.

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Application of Artificial Intelligence in Early Gastric Cancer Diagnosis

Digestion ◽

10.1159/000519601 ◽

2021 ◽

pp. 1-7

Author(s):

Zili Xiao ◽

Danian Ji ◽

Feng Li ◽

Zhengliang Li ◽

Zhijun Bao

Keyword(s):

Artificial Intelligence ◽

Gastric Cancer ◽

New Technologies ◽

Narrow Band Imaging ◽

Daily Practice ◽

Colorectal Polyps ◽

Current Status ◽

Randomized Controlled ◽

Depth Prediction ◽

Substantial Effort

Background: With the development of new technologies such as magnifying endoscopy with narrow band imaging, endoscopists achieved better accuracy for diagnosis of gastric cancer (GC) in various aspects. However, to master such skill takes substantial effort and could be difficult for inexperienced doctors. Therefore, a novel diagnostic method based on artificial intelligence (AI) was developed and its effectiveness was confirmed in many studies. AI system using convolutional neural network has showed marvelous results in the ongoing trials of computer-aided detection of colorectal polyps. Summary: With AI’s efficient computational power and learning capacities, endoscopists could improve their diagnostic accuracy and avoid the overlooking or over-diagnosis of gastric neoplasm. Several systems have been reported to achieved decent accuracy. Thus, AI-assisted endoscopy showed great potential on more accurate and sensitive ways for early detection, differentiation, and invasion depth prediction of gastric lesions. However, the feasibility, effectiveness, and safety in daily practice remain to be tested. Key messages: This review summarizes the current status of different AI applications in early GC diagnosis. More randomized controlled trails will be needed before AI could be widely put into clinical practice.

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