From Early Morphometrics to Machine Learning—What Future for Cardiovascular Imaging of the Pulmonary Circulation?

Gopalan, Deepa; Gibbs, J. Simon R.

doi:10.3390/diagnostics10121004

Cited by 5 publications

(3 citation statements)

References 128 publications

(140 reference statements)

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“…Traditionally, analyzing these images relied on manual segmentation of anatomical structures, a time-consuming and subjective process [17]. However, the emergence of deep learning has ushered in a new era of automated segmentation, offering the potential to revolutionize how we analyze echocardiograms [18,19].…”

Section: Related Workmentioning

confidence: 99%

Automated Left Ventricle Segmentation in Echocardiography using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment

Balasubramani,

Sung,

Hsieh

et al. 2024

Preprint

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Accurate segmentation of the Left Ventricle (LV) in Echocardiogram (Echo) images is essential for cardiovascular analysis. Conventional techniques are labor-intensive and exhibit inter-observer variability. Deep learning has emerged as a powerful tool for automated medical image segmentation, offering advantages in speed and potentially superior accuracy. This study explores the efficacy of employing the YOLO (You Only Look Once) segmentation model for automated LV segmentation in Echo images. YOLO, a cutting-edge object detection model, achieves exceptional speed-accuracy balance through its well-designed architecture. It utilizes efficient dilated convolutional layers and bottleneck blocks for feature extraction, while incorporating innovations like path aggregation and spatial attention mechanisms. These attributes make YOLO a compelling candidate for adaptation to LV segmentation in Echo images. We posit that by fine-tuning a pre-trained YOLO based model on a well-annotated Echo image dataset, we can leverage the model's strengths in real-time processing and precise object localization to achieve robust LV segmentation. The proposed approach entails fine-tuning a pre-trained YOLO model on a rigorously labeled Echo image dataset. Model performance has been evaluated using established metrics such as mean Average Precision (mAP) at an Intersection over Union (IoU) threshold of 50% (mAP50) with 98.31%and across a range of IoU thresholds from 50% to 95% (mAP50:95) with 75.27%. Successful implementation of YOLO for LV segmentation has the potential to significantly expedite and standardize Echo image analysis. This advancement could translate to improved clinical decision-making and enhanced patient care.

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Section: Related Workmentioning

confidence: 99%

Automated Left Ventricle Segmentation in Echocardiography using YOLO: A Deep Learning Approach for Enhanced Cardiac Function Assessment

Balasubramani,

Sung,

Hsieh

et al. 2024

Preprint

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“…43,44 Quantitative analysis of CT, now possible with digital imaging methods, eliminates human errors due to subjective visual assessment. 45 Such an approach seems particularly useful for patients in whom PH is associated or coexists with lung diseases. 46 Deep phenotyping, particularly focused on changes in metabolome, may have a role in the diagnosis…”

Section: Final Comments and Future Perspectivesmentioning

confidence: 99%

The Diagnostic Approach to Pulmonary Hypertension

Torbicki¹,

Kurzyna²

2023

Semin Respir Crit Care Med

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The clinical presentation of pulmonary hypertension (PH) is nonspecific, resulting in significant delays in its detection. In the majority of cases, PH is a marker of the severity of other cardiopulmonary diseases. Differential diagnosis aimed at the early identification of patients with pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) who do require specific and complex therapies is as important as PH detection itself. Despite all efforts aimed at the noninvasive assessment of pulmonary arterial pressure, the formal confirmation of PH still requires catheterization of the right heart and pulmonary artery. The current document will give an overview of strategies aimed at the early diagnosis of PAH and CTEPH, while avoiding their overdiagnosis. It is not intended to be a replica of the recently published European Society of Cardiology (ESC) and European Respiratory Society (ERS) Guidelines on Diagnosis and Treatment of Pulmonary Hypertension, freely available at the Web sites of both societies. While promoting guidelines' recommendations, including those on new definitions of PH, we will try to bring them closer to everyday clinical practice, benefiting from our personal experience in managing patients with suspected PH.

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“…The first section contains five papers dedicated to problems related to overlap of vascular and respiratory problems in the lungs-Olschewski's perspective from Ludwig Boltzmann Institute, University of Graz [2] is followed by a review of the state of the art in PAH associated with sclerodermia by Naranjo and Hassoun, from John Hopkins University, Baltimore [3]. The modern approach to thoracic imaging in patients with PH, including machine learning and artificial intelligence, is covered by outstanding reviews from a team from Sheffield University [4], and Gibbs and Gopalan from Imperial College, London and Cambridge University, respectively [5], while an attempt to link genetic background with radiological phenotyping in PVOD is reported from Hospital Universitario Doce de Octubre in Madrid [6].…”

mentioning

confidence: 99%