Evidence map›Paper›PMID 41642499›Full record

SynthesisNeurosurgical review2026

Prediction of remission in cushing's disease using artificial intelligence: A systematic review and meta-analysis.

Mohammadamin Sabbagh Alvani, Ibrahim Mohammadzadeh, Bardia Hajikarimloo, Sai Sanikommu, Ali Mortezaei, Pooya Eini, Shahin Mohammadzadeh, Mohammad Amin Habibi, Seyed Ali Mousavinejad, Vratko Himic and 2 more

Abstract readSystematic ReviewMeta-Analysis
PubMed Publisher
In one paragraph

Synthesis in Neurosurgical review, 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

12 authors.

Mohammadamin Sabbagh Alvani *Department of Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Ibrahim Mohammadzadeh *Department of Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Bardia HajikarimlooDepartment of Neurological Surgery, University of Virginia, Charlottesville, VA, USA.
Sai SanikommuDepartment of Neurological Surgery, Radiology, Neurosciences, Pharmacology, University of Miami School of Medicine, Miami, FL, USA.
Ali MortezaeiStudent Research Committee, Gonabad University of Medical Sciences, Gonabad, Iran.
Pooya EiniCardiovascular Research Center, Rajaie Cardiovascular Institute, Tehran, Iran.
Shahin MohammadzadehDepartment of Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mohammad Amin HabibiDepartment of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Seyed Ali MousavinejadDepartment of Skull Base Research Center, Loghman-Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. alimousavinejad65@gmail.com.
Vratko HimicDepartment of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA.
Antonio Di IevaComputational NeuroSurgery (CNS) Lab, Faculty of Medicine, Health and Human Sciences, Macquarie Medical School, Macquarie University, Sydney, Australia.
Ricardo J KomotarDepartment of Neurological Surgery, University of Miami Miller School of Medicine, Miami, FL, USA. rkomotar@med.miami.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting remission in Cushing’s disease (CD) following transsphenoidal surgery (TSS) is crucial for improving treatment strategies and patient outcomes. This systematic review and meta-analysis aimed to evaluate the application of Artificial Intelligence (AI) algorithms in forecasting remission outcomes, integrating diverse clinical, biochemical, and imaging data to enhance predictive accuracy. A comprehensive search was conducted across PubMed, Web of Science, Embase, Scopus, and Google Scholar databases up to December 2024, adhering to PRISMA guidelines. Out of 1,571 records identified, five studies involving 1,938 patients met the inclusion criteria. The pooled sensitivity and specificity of Machine learning (ML) models were 0.93 [95% CI: 0.65–0.99] and 0.78 [95% CI: 0.50–0.93], respectively, with the diagnostic score was 3.8 [95% CI: 0.86–6.74] and diagnostic odds ratio (DOR) of 44.79 [95% CI: 2.37–846.66]. The positive diagnostic likelihood ratio (DLR) was 4.23 [95% CI: 1.39–12.86], while the negative DLR was 0.09 [95% CI: 0.01–0.67]. The area under the curve (AUC) was 0.91. These results underscore the significant potential of AI algorithms in enhancing clinical decision-making and improving the prediction of remission in CD. However, methodological heterogeneity and the lack of external validation across studies call for standardized approaches to ensure broader applicability and reliability of these models in clinical settings.

Indexed as

Artificial IntelligencePituitary ACTH HypersecretionHumansMachine LearningPrediction AlgorithmsPredictive Learning ModelsRemission InductionACTH secreting tumorArtificial intelligenceCushing diseaseDeep learningMachine learningOutcomePredictors

Identifiers

PMID41642499

What OpenQuestion holds

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Registered trials

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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.