Ashnil Kumar
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Explore the profile of Ashnil Kumar including associated specialties, affiliations and a list of published articles.
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36
Citations
295
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Recent Articles
1.
Taylor A, Habib A, Kumar A, Wong E, Hasan Z, Singh N
Clin Otolaryngol
. 2023 Jul;
48(6):888-894.
PMID: 37488094
Background: Classifying sphenoid pneumatisation is an important but often overlooked task in reporting sinus CT scans. Artificial intelligence (AI) and one of its key methods, convolutional neural networks (CNNs), can...
2.
Wang J, Lillia J, Kumar A, Bray P, Kim J, Burns J, et al.
BMC Bioinformatics
. 2022 Oct;
23(1):431.
PMID: 36253726
Background: Predicting morphological changes to anatomical structures from 3D shapes such as blood vessels or appearance of the face is a growing interest to clinicians. Machine learning (ML) has had...
3.
Fu X, Bi L, Kumar A, Fulham M, Kim J
IEEE Trans Med Imaging
. 2022 Jun;
41(11):3266-3277.
PMID: 35679380
The identification of melanoma involves an integrated analysis of skin lesion images acquired using clinical and dermoscopy modalities. Dermoscopic images provide a detailed view of the subsurface visual structures that...
4.
Hasan Z, Key S, Habib A, Wong E, Aweidah L, Kumar A, et al.
Ann Otol Rhinol Laryngol
. 2022 Jun;
132(4):417-430.
PMID: 35651308
Introduction: Convolutional neural networks (CNNs) represent a state-of-the-art methodological technique in AI and deep learning, and were specifically created for image classification and computer vision tasks. CNNs have been applied...
5.
Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta-analysis
Habib A, Kajbafzadeh M, Hasan Z, Wong E, Gunasekera H, Perry C, et al.
Clin Otolaryngol
. 2022 Mar;
47(3):401-413.
PMID: 35253378
Objectives: To summarise the accuracy of artificial intelligence (AI) computer vision algorithms to classify ear disease from otoscopy. Design: Systematic review and meta-analysis. Methods: Using the PRISMA guidelines, nine online...
6.
Habib A, Crossland G, Patel H, Wong E, Kong K, Gunasekera H, et al.
Otol Neurotol
. 2022 Mar;
43(4):481-488.
PMID: 35239622
Objective: To develop an artificial intelligence image classification algorithm to triage otoscopic images from rural and remote Australian Aboriginal and Torres Strait Islander children. Study Design: Retrospective observational study. Setting:...
7.
Xia T, Kumar A, Fulham M, Feng D, Wang Y, Kim E, et al.
Sci Rep
. 2022 Feb;
12(1):2173.
PMID: 35140267
Radiogenomics relationships (RRs) aims to identify statistically significant correlations between medical image features and molecular characteristics from analysing tissue samples. Previous radiogenomics studies mainly relied on a single category of...
8.
Peng Y, Bi L, Kumar A, Fulham M, Feng D, Kim J
Phys Med Biol
. 2021 Nov;
66(24).
PMID: 34818637
Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of soft-tissue sarcomas (STSs). Distant metastases (DM) are the leading cause of death in...
9.
Fu X, Bi L, Kumar A, Fulham M, Kim J
IEEE J Biomed Health Inform
. 2021 Feb;
25(9):3507-3516.
PMID: 33591922
Multimodal positron emission tomography-computed tomography (PET-CT) is used routinely in the assessment of cancer. PET-CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. Tumor...
10.
Ahn E, Kumar A, Fulham M, Feng D, Kim J
IEEE Trans Med Imaging
. 2020 Feb;
39(7):2385-2394.
PMID: 32012005
The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale labelled training data. In medical imaging, these large labelled datasets are sparse,...