Evidence map›Paper›PMID 39959047›Full record

ReviewACS omega2025

Advancements in Breathomics: Special Focus on Electrochemical Sensing and AI for Chronic Disease Diagnosis and Monitoring.

Nikini Rashmithara Subawickrama Mallika Widanaarachchige, Anirban Paul, Ivneet Kaur Banga, Ashlesha Bhide, Sriram Muthukumar, Shalini Prasad

Abstract readReview
In one paragraph

Review in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Nikini Rashmithara Subawickrama Mallika WidanaarachchigeDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas 75080, United States.
Anirban PaulDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas 75080, United States.
Ivneet Kaur BangaDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas 75080, United States.
Ashlesha BhideDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas 75080, United States.ORCID https://orcid.org/0009-0001-2473-0177
Sriram MuthukumarDepartment of Materials Science and Engineering, University of Texas at Dallas, Richardson, Texas 75080, United States.ORCID https://orcid.org/0000-0002-8761-7278
Shalini PrasadDepartment of Bioengineering, University of Texas at Dallas, Richardson, Texas 75080, United States.ORCID https://orcid.org/0000-0002-2404-3801

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This Review examines the potential of breathomics in enhancing disease monitoring and diagnostic precision when integrated with artificial intelligence (AI) and electrochemical sensing techniques. It discusses breathomics' potential for early and noninvasive disease diagnosis with a focus on chronic kidney disease (CKD), chronic obstructive pulmonary disease (COPD), and lung cancer, which have been well studied in the context of VOC association with diseases. The noninvasive nature of exhaled breath analysis can be advantageous compared to traditional diagnostic methods for CKD, which often rely on blood and urine testing. VOC analysis can enhance spirometry and imaging methods used in COPD diagnosis, providing a more comprehensive picture of the disease's progression. Breathomics could also provide a less intrusive and potentially earlier diagnostic approach for lung cancer, which is now dependent on imaging and biopsy. The combination of breathomics, electrochemical sensing, and AI could lead to more personalized and successful treatment plans for chronic illnesses using AI algorithms to decipher complicated VOC patterns. This Review assesses the viability and effectiveness of combining breathomics with electrochemical sensors and artificial intelligence by synthesizing recent research findings and technological developments.

Identifiers

PMID39959047
PMCPMC11822511

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.