ArticleFrontiers in endocrinology2020
Identifying Facial Features and Predicting Patients of Acromegaly Using Three-Dimensional Imaging Techniques and Machine Learning.
Article in Frontiers in endocrinology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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
25 citing papers in PubMed, 52 citations in OpenAlex.
- Delayed diagnosis of acromegaly when lower facial features were obscured by masks during the COVID-19 pandemic.JCEM case reports · 2026Article
- Automatic acromegaly detection using deep learning on hand images: a multicenter observational study.The Journal of clinical endocrinology and metabolism · 2026Observational
- Acromegaly diagnosis.The Journal of clinical endocrinology and metabolism · 2026Review
- Current controversies in acromegaly care.The Journal of clinical endocrinology and metabolism · 2026Review
- Detection of Acromegaly From Facial Images Using Machine Learning: A Comparison With Clinical Experts.Journal of the Endocrine Society · 2026Article
- Potential role of insulin-like growth factor 1 and growth hormone in acneiform eruptions: evidence from a rat model of acromegaly.Journal of endocrinological investigation · 2026Article
- Enhanced temporal encoding-decoding for survival analysis of multimodal clinical data in smart healthcare.Visual computing for industry, biomedicine, and art · 2025Article
- Correlation Between Volumetric Soft Tissue Asymmetry and Postero-Anterior Cephalometric Measurements in Patients with Skeletal Facial Asymmetry: A Cross-Sectional Pilot Study.Journal of clinical medicine · 2025Article
- Artificial intelligence in acromegaly: Why, when and how.Pituitary · 2025Article
- Quality of life and psychological status in people with acromegaly in relation to facial changes.Endocrine connections · 2025Article
- Acromegaly: diagnostic challenges and individualized treatment.Expert review of endocrinology & metabolism · 2025Review
- Three-dimensional facial features of suicide risk in females with depression.Frontiers in psychiatry · 2025Article
- Artificial intelligence-driven approaches in pituitary neuroendocrine tumors: integrating endocrine-metabolic profiling for enhanced diagnostics and therapeutics.Frontiers in endocrinology · 2025Review
- Quantifying Dermatochalasis Using 3-Dimensional Photogrammetry.Aesthetic plastic surgery · 2024Article
- Article
- Current and Future Advances in Surgical Therapy for Pituitary Adenoma.Endocrine reviews · 2023Article
- Using 2-dimensional hand photographs to predict postoperative biochemical remission in acromegaly patients: a transfer learning approach.Quantitative imaging in medicine and surgery · 2023Article
- Skin manifestations and submacroscopical features of acromegaly: A case-control study using dermoscopy and high-frequency ultrasound.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2023Observational
- Temporal and periorbital depressions identified by 3D images are correlated with malnutrition phenotypes in cancer patients: A pilot study.Frontiers in nutrition · 2023Article
- Objective study of the facial parameters of observations in patients with type 2 diabetes mellitus by machine learning.Annals of translational medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
PubMed holds no abstract for this paper.
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.