Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images

Chauhan, Daksh; Anyanwu, Emeka; Goes, Jacob; Besser, Stephanie; Anand, Simran; Madduri, Ravi; Getty, Neil; Kelle, Sebastian; Kawaji, Keigo; Mor‐Avi, Victor; Patel, Amit R.

doi:10.1016/j.clinimag.2021.11.013

Cited by 14 publications

(12 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among those, computer-aided imaging techniques are a good example. In general, it is not necessary for a clinician to get an explanation when studying an MRI or tomography 60 , 61 , 62 ; it is sufficient to understand the basics of the technology, particularly its limitations, sensitivity, and specificity, to be able to extract the necessary value. This could also be true of analog technologies, where understanding of the physics of electricity, sounds, or pressure is not necessary to interpret an electrocardiogram, heart sounds using a stethoscope, or simple measurement of blood pressure.…”

Section: Resultsmentioning

confidence: 99%

Human-centered explainability for life sciences, healthcare, and medical informatics

et al. 2022

View full text Add to dashboard Cite

Section: Resultsmentioning

confidence: 99%

Human-centered explainability for life sciences, healthcare, and medical informatics

et al. 2022

View full text Add to dashboard Cite

“…The output variables are predicted or classified from the training database [10]. This means that algorithms use the ground truths and labelled segmentation maps to try and learn some patterns in the data during training, so as to implement these learnt patterns to the testing dataset [19]. This therefore provides results in relation to the learnt patterns (Figure 7).…”

Section: Application Of Ai Methods In Magnetite Spectral Classificati...mentioning

confidence: 99%

“…ML algorithms are mathematical engines of AI, which means these algorithms attribute their classification abilities to perceived mathematical relationships present within the data [19]. In simple terms, ML algorithms try to fit the data within a particular pattern, which can be described using mathematical functions.…”

Section: How Machine Learning Algorithms Operatementioning

confidence: 99%

“…DL algorithms, are far more sophisticated in their architecture compared to ML algorithms [10]. They are complex and multilayered, and are built to allow data to pass through information processing layers (like a human brain neurons) in highly connected ways; the result is a non-linear transformation of data which cannot be described by a mathematical function [10,18,19]. It does however take a large amount of data compared to ML to train DL algorithms in order to attain high accuracy output models [19].…”

Section: How Deep Learning Algorithms Operatementioning

confidence: 99%

“…AI algorithms such as ML and DL possess great spectral image classification power [18,19], allowing one to resolve qualitative as well as quantitative data in an objective and understandable manner, making it highly eligible in multiple industry applications. This study's integrated system has the potential to enhance numerous aspects of the mining chain, hence the motivation for this study.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

et al. 2022

View full text Add to dashboard Cite

The use of drones in mining environments is one way in which data pertaining to the state of a site in various industries can be remotely collected. This paper proposes a combined system that employs a 6-bands multispectral image capturing camera mounted on an Unmanned Aerial Vehicle (UAV) drone, Spectral Angle Mapping (SAM), as well as Artificial Intelligence (AI). Depth possessing multispectral data were captured at different flight elevations. This was in an attempt to find the best elevation where remote identification of magnetite iron sands via the UAV drone specialized in collecting spectral information at a minimum accuracy of +/− 16 nm was possible. Data were analyzed via SAM to deduce the cosine similarity thresholds at each elevation. Using these thresholds, AI algorithms specialized in classifying imagery data were trained and tested to find the best performing model at classifying magnetite iron sand. Considering the post flight logs, the spatial area coverage of 338 m2, a global classification accuracy of 99.7%, as well the per-class precision of 99.4%, the 20 m flight elevation outputs presented the best performance ratios overall. Thus, the positive outputs of this study suggest viability in a variety of mining and mineral engineering practices.

show abstract

Detection of hypoplastic left heart syndrome anatomy from cardiovascular magnetic resonance images using machine learning

Gabbert,

Petersen,

Burleigh

et al. 2024

Magn Reson Mater Phy

View full text Add to dashboard Cite

Objective The prospect of being able to gain relevant information from cardiovascular magnetic resonance (CMR) image analysis automatically opens up new potential to assist the evaluating physician. For machine-learning-based classification of complex congenital heart disease, only few studies have used CMR. Materials and methods This study presents a tailor-made neural network architecture for detection of 7 distinctive anatomic landmarks in CMR images of patients with hypoplastic left heart syndrome (HLHS) in Fontan circulation or healthy controls and demonstrates the potential of the spatial arrangement of the landmarks to identify HLHS. The method was applied to the axial SSFP CMR scans of 46 patients with HLHS and 33 healthy controls. Results The displacement between predicted and annotated landmark had a standard deviation of 8–17 mm and was larger than the interobserver variability by a factor of 1.1–2.0. A high overall classification accuracy of 98.7% was achieved. Discussion Decoupling the identification of clinically meaningful anatomic landmarks from the actual classification improved transparency of classification results. Information from such automated analysis could be used to quickly jump to anatomic positions and guide the physician more efficiently through the analysis depending on the detected condition, which may ultimately improve work flow and save analysis time.

show abstract

Comparison of machine learning and deep learning for view identification from cardiac magnetic resonance images

Cited by 14 publications

References 11 publications

Human-centered explainability for life sciences, healthcare, and medical informatics

Human-centered explainability for life sciences, healthcare, and medical informatics

Spectral Angle Mapping and AI Methods Applied in Automatic Identification of Placer Deposit Magnetite Using Multispectral Camera Mounted on UAV

Detection of hypoplastic left heart syndrome anatomy from cardiovascular magnetic resonance images using machine learning

Contact Info

Product

Resources

About