Trial reportScientific reports2021
Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis.
Trial report in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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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.
Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advancing hirschsprung disease diagnosis: a systematic review of the development and application of artificial intelligence in histopathological analysis.Pediatric surgery international · 2026Pooled it
- Machine learning in the diagnosis of Hirschsprung disease: a systematic review and meta-analysis.Pediatric surgery international · 2026Pooled it
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- circANKRD12/circTIMMDC1 synergistically regulates enteric neural crest cell migration via miR-181b-5p-PROX1-NOTCH1 axis in Hirschsprung's disease.Pediatric research · 2026Article
- Latest Developments in Artificial Intelligence and Machine Learning Models in General Pediatric Surgery.European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie · 2026Review
- Current and future state of the management of Hirschsprung disease.World journal of pediatric surgery · 2025Review
- Automatic analysis of nuclear features reveals a non-tumoral predictor of tumor grade in bladder cancer.Diagnostic pathology · 2024Article
- Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners.Diagnostic pathology · 2024Article
- Enhancing diagnosis of Hirschsprung's disease using deep learning from histological sections of post pull-through specimens: preliminary results.Pediatric surgery international · 2023Article
- Hirschsprung disease.Nature reviews. Disease primers · 2023Review
- Perineural invasion detection in pancreatic ductal adenocarcinoma using artificial intelligence.Scientific reports · 2023Article
- A new systematization of histological analysis for the diagnosis of Hirschsprung's disease.Clinics (Sao Paulo, Brazil) · 2023Article
- Computational pathology in bladder cancer: A scoping review.Bladder cancer (Amsterdam, Netherlands)Review
- Detection of Muscularis propria Invasion in Urothelial Carcinoma Using Artificial Intelligence.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histopathologic diagnosis of Hirschsprung's disease (HSCR) is time consuming and requires expertise. The use of artificial intelligence (AI) in digital pathology is actively researched and may improve the diagnosis of HSCR. The purpose of this research was to develop an algorithm capable of identifying ganglion cells in digital pathology slides and implement it as an assisting tool for the pathologist in the diagnosis of HSCR. Ninety five digital pathology slides were used for the construction and training of the algorithm. Fifty cases suspected for HSCR (727 slides) were used as a validation cohort. Image sets suspected to contain ganglion cells were chosen by the algorithm and then reviewed and scored by five pathologists, one HSCR expert and 4 non-experts. The algorithm was able to identify ganglion cells with 96% sensitivity and 99% specificity (in normal colon) as well as to correctly identify a case previously misdiagnosed as non-HSCR. The expert was able to achieve perfectly accurate diagnoses based solely on the images suggested by the algorithm, with over 95% time saved. Non-experts would require expert consultation in 20-58% of the cases to achieve similar results. The use of AI in the diagnosis of HSCR can greatly reduce the time and effort required for diagnosis and improve accuracy.
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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.