Evidence mapPaperPMID 41930907Full record

ReviewVeterinary medicine and science2026

Advances in Avian Diagnostic Pathology: Current Trends, Challenges and Future Directions: A Review.

Gebyaw Menge Getnet, Mengesha Ayehu Getnet, Ayenalem Shibabaw Atenaf

Abstract readReview
In one paragraph

Review in Veterinary medicine and science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Gebyaw Menge GetnetDepartment of Veterinary Science, College of Agriculture and Natural Resource, Debre Markos University, Debre Markos, Ethiopia.ORCID https://orcid.org/0009-0002-0214-5564
Mengesha Ayehu GetnetDepartments of Veterinary Pathobiology, College of Veterinary Medicine and Animal Science, University of Gondar, Gondar, Ethiopia.ORCID https://orcid.org/0009-0000-7806-1389
Ayenalem Shibabaw AtenafDepartments of Veterinary Pathobiology, College of Veterinary Medicine and Animal Science, University of Gondar, Gondar, Ethiopia.ORCID https://orcid.org/0009-0009-9501-4555

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Avian pathology is the scientific study of diseases in birds, focusing on the structural, functional and molecular changes in tissues and organs caused by infections, toxins, nutritional deficiencies or others. It plays a critical role in maintaining poultry health, ensuring food security and supporting economic growth. The aim of this review is to highlight current trends, challenges and future directions in avian pathology, with a special emphasis on advancements in diagnostic approaches that enhance avian disease detection and management. However, the practical application of advanced technologies in avian pathology remains limited, particularly in Ethiopia. Recent diagnostic advancements, including immunohistochemistry, molecular techniques as well as digital pathology, have improved the detection, characterisation and management of poultry diseases. Future directions emphasise the use of artificial intelligence (AI) and machine learning for accurate diagnostics, real-time disease monitoring and outbreak prediction. Ethiopia has achieved significant progress in avian pathology, particularly through polymerase chain reaction and histopathology. Despite ongoing advancements, the poultry industry continues to face challenges, including emerging and re-emerging pathogens, limited access to diagnostic infrastructure, zoonotic risks and antimicrobial resistance. Therefore, strengthening biosecurity practices, promoting responsible antimicrobial use and expanding the use of molecular, digital pathology and AI-supported diagnostic tools remain essential strategies for protecting both poultry population and public health. To further enhance disease detection and control, diagnostic capacity and professional training in avian pathology should be strengthened in Ethiopia.

Indexed as

Pathology, VeterinaryPoultry DiseasesAnimalsEthiopiaPoultryartificial intelligenceavian pathologydiagnosticsdigital pathologypoultry

Identifiers

PMID41930907
PMCPMC13051840

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 Socratic graph.