Samuel Budd

686 total citations · 1 hit paper
6 papers, 359 citations indexed

About

Samuel Budd is a scholar working on Pediatrics, Perinatology and Child Health, Health Informatics and Epidemiology. According to data from OpenAlex, Samuel Budd has authored 6 papers receiving a total of 359 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Pediatrics, Perinatology and Child Health, 3 papers in Health Informatics and 3 papers in Epidemiology. Recurrent topics in Samuel Budd's work include Fetal and Pediatric Neurological Disorders (4 papers), Artificial Intelligence in Healthcare and Education (3 papers) and Congenital Heart Disease Studies (3 papers). Samuel Budd is often cited by papers focused on Fetal and Pediatric Neurological Disorders (4 papers), Artificial Intelligence in Healthcare and Education (3 papers) and Congenital Heart Disease Studies (3 papers). Samuel Budd collaborates with scholars based in United Kingdom, Germany and United States. Samuel Budd's co-authors include Bernhard Kainz, Emma C. Robinson, Joseph V. Hajnal, Jacqueline Matthew, Thomas G. Day, Reza Razavi, Alberto Gómez, Trisha V. Vigneswaran, Emily Skelton and Jeremy Tan and has published in prestigious journals such as Medical Image Analysis, Ultrasound in Obstetrics and Gynecology and npj Digital Medicine.

In The Last Decade

Samuel Budd

5 papers receiving 356 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A survey on active learning and human-in-the-loop deep learning for medical image analysis

2021 336 citations Samuel Budd, Emma C. Robinson et al. Medical Image Analysis profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Samuel Budd United Kingdom	4	173	111	69	39	29	6	359
Nicola Rieke United Kingdom	5	326 1.9×	149 1.3×	90 1.3×	38 1.0×	55 1.9×	11	454
Pawan Kumar Mall India	7	110 0.6×	117 1.1×	83 1.2×	40 1.0×	24 0.8×	13	292
Abrar Almjally Saudi Arabia	7	122 0.7×	79 0.7×	107 1.6×	33 0.8×	16 0.6×	19	415
Megha Bhushan India	14	200 1.2×	135 1.2×	65 0.9×	39 1.0×	14 0.5×	39	488
Xiaozheng Xie China	5	131 0.8×	128 1.2×	93 1.3×	34 0.9×	13 0.4×	12	344
Amer Malki Saudi Arabia	11	150 0.9×	88 0.8×	61 0.9×	22 0.6×	11 0.4×	19	350
Adnan N. Qureshi Pakistan	10	154 0.9×	124 1.1×	83 1.2×	24 0.6×	12 0.4×	42	335
Harold Brayan Arteaga-Arteaga Colombia	10	93 0.5×	77 0.7×	98 1.4×	25 0.6×	12 0.4×	17	290
Jordina Torrents‐Barrena Spain	10	83 0.5×	112 1.0×	44 0.6×	24 0.6×	33 1.1×	28	324
Rayan Krishnan United States	3	157 0.9×	123 1.1×	64 0.9×	43 1.1×	64 2.2×	3	385

Countries citing papers authored by Samuel Budd

Since Specialization

Citations

This map shows the geographic impact of Samuel Budd's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Samuel Budd with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Samuel Budd more than expected).

Fields of papers citing papers by Samuel Budd

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Samuel Budd. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Samuel Budd. The network helps show where Samuel Budd may publish in the future.

Co-authorship network of co-authors of Samuel Budd

This figure shows the co-authorship network connecting the top 25 collaborators of Samuel Budd. A scholar is included among the top collaborators of Samuel Budd based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Samuel Budd. Samuel Budd is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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6 of 6 papers shown

1.

Day, Thomas G., Samuel Budd, Robert Wright, et al.. (2025). Video Clip Extraction From Fetal Ultrasound Scans Using Artificial Intelligence to Allow Remote Second Expert Review for Congenital Heart Disease. Prenatal Diagnosis. 45(4). 531–538.

2.

Budd, Samuel, Robert Wright, Jacqueline Matthew, et al.. (2025). Whole examination AI estimation of fetal biometrics from 20-week ultrasound scans. npj Digital Medicine. 8(1). 22–22. 3 indexed citations

3.

Day, Thomas G., Jacqueline Matthew, Samuel Budd, et al.. (2024). Interaction between clinicians and artificial intelligence to detect fetal atrioventricular septal defects on ultrasound: how can we optimize collaborative performance?. Ultrasound in Obstetrics and Gynecology. 64(1). 28–35. 7 indexed citations

4.

Day, Thomas G., Samuel Budd, Jeremy Tan, et al.. (2023). Prenatal diagnosis of hypoplastic left heart syndrome on ultrasound using artificial intelligence: How does performance compare to a current screening programme?. Prenatal Diagnosis. 44(6-7). 717–724. 9 indexed citations

5.

Budd, Samuel, et al.. (2021). Prototyping CRISP: A Causal Relation and Inference Search Platform applied to Colorectal Cancer Data. Research Portal (King's College London). 517–521. 4 indexed citations

6.

Budd, Samuel, Emma C. Robinson, & Bernhard Kainz. (2021). A survey on active learning and human-in-the-loop deep learning for medical image analysis. Medical Image Analysis. 71. 102062–102062. 336 indexed citations breakdown →

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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