ArticleMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention2019
Contextual Deep Regression Network for Volume Estimation in Orbital CT.
Article in Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Secondary use of health records for prediction, detection, and treatment planning in the clinical decision support system: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications.PeerJ. Computer science · 2024Article
- Prediction of Type II Diabetes Onset with Computed Tomography and Electronic Medical Records.Multimodal learning for clinical decision support and clinical image-based procedures : 10th International Workshop, ML-CDS 2020, and 9th International Workshop, CLIP 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4-8, ... · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Diseases of the optic nerve cause structural changes observable through clinical computed tomography (CT) imaging. Previous work has shown that multi-atlas methods can be used to segment and extract volumetric measurements from the optic nerve, which are associated with visual disability and disease. In this work, we trained a weakly supervised convolutional neural network to learn optic nerve volumes directly, without segmentation. Furthermore, we explored the role of contextual electronic medical record (EMR) information, specifically ICD-9 codes, to improve optic nerve volume estimation. We constructed a merged network to combine data from imaging as well as EMR and demonstrated that context improved volume prediction, with a 15% increase in explained-variance (
Indexed as
Identifiers
What OpenQuestion holds
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.