Evidence map›Paper›PMID 30949431›Full record

ReviewIntegrative medicine research2019

Advances in automated tongue diagnosis techniques.

Marzia Hoque Tania, Khin Lwin, Mohammed Alamgir Hossain

Registry-linked trialAbstract readReview
In one paragraph

Review in Integrative medicine research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04559958 (Tongue Inspection and Diagnosis for Chronic Kidney Disease), which is not on this map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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.

NCT04559958 completednot on this map

Tongue Inspection and Diagnosis for Chronic Kidney Disease: Protocol for a Cross-sectional, Case-controlled Observational Study

TypeobservationalSponsorChang Gung Memorial HospitalRan2018 to 2020Enrolled120ConditionsChronic Kidney DiseasesArmstongue image
3 · Its place in the literature

Who cites it

26 citing papers in PubMed.

  1. Article
  2. Article
  3. Recent advances in smart contact lenses.Biosensors & bioelectronics: X · 2026
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  5. Review
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  11. Review
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  14. Review
  15. Article
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  17. Article
  18. Article
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  20. Observational
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

3 authors.

Marzia Hoque TaniaAnglia Ruskin IT Research Institute, Anglia Ruskin University, Chelmsford, UK.
Khin LwinAnglia Ruskin IT Research Institute, Anglia Ruskin University, Chelmsford, UK.
Mohammed Alamgir HossainAnglia Ruskin IT Research Institute, Anglia Ruskin University, Chelmsford, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the primary healthcare system. This work reviews the recent advances in tongue diagnosis, which is a significant constituent of traditional oriental medicinal technology, and explores the literature to evaluate the works done on the various aspects of computerized tongue diagnosis, namely preprocessing, tongue detection, segmentation, feature extraction, tongue analysis, especially in traditional Chinese medicine (TCM). In spite of huge volume of work done on automatic tongue diagnosis (ATD), there is a lack of adequate survey, especially to combine it with the current diagnosis trends. This paper studies the merits, capabilities, and associated research gaps in current works on ATD systems. After exploring the algorithms used in tongue diagnosis, the current trend and global requirements in health domain motivates us to propose a conceptual framework for the automated tongue diagnostic system on mobile enabled platform. This framework will be able to connect tongue diagnosis with the future point-of-care health system.

Indexed as

Automated tongue diagnosisClinical decision support systemsImage processingMachine learningMobile-enabled systems

Identifiers

PMID30949431
PMCPMC6428917

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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.