Development of Cloud-Based Telemedicine Platform for Acute Intracerebral Hemorrhage in Gangwon-do : Concept and Protocol

Jun, Hyo Sub; Yang, Kuhyun; Kim, Jong Yeon; Jeon, Jin Pyeong; Ahn, Jun Hyong; Lee, Seung Jin; Choi, Hyuk Jai; Choi, Jong Wook; Cho, Sung Min; Rhim, Jong Kook

doi:10.3340/jkns.2022.0256

Cited by 3 publications

(4 citation statements)

References 22 publications

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“…However, they noted that their computational limitations hindered processing CT slices at their original scale, potentially affecting diagnostic precision and quality [9]. The paper [11] provided a cloud based telemedicine platform for patients with intracerebral haemorrhage detection in rural and underserved areas. For evaluating daily evapotranspiration estimation, the authors [10] developed the stacking/blending ensemble method.…”

Section: Related Workmentioning

confidence: 99%

Cloud based Stacking and Bending Ensemble Approach for the classification of Brain Haemorrhage using Meta Diverse Model

Seeli,

Thanammal

2024

Preprint

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In the medical field, disease diagnosis through cloud computing is a promising technology to focus more on patient care. It offers benefits for both patients and doctors. We aimed to develop a cloud based classification method for patients with brain haemorrhage in rural (or) remote areas. The detection of brain haemorrhage is critical for neurological diagnosis and treatment. Although deep learning models have shown promise in medical image analysis, predicting brain haemorrhage remains challenging due to its complexity and variability. To address this, we propose an ensemble approach for detection of Intracranial Haemorrhage diagnosis using cloud computing. Cloud services enable data to be stored on remote servers and then be accessed via the Internet. The user does not need to be in a specific location to access it, which allows the user to work remotely. The ensemble comprises diverse deep learning models (ResNet50, VGG16, and DenseNet121) with Global Average Pooling 2D layers, benefiting from the cloud's computational resources for efficient training. Each model brings unique strengths, enabling us to capture a wider range of data patterns and improve prediction accuracy. Data pre-processing involves dividing the dataset into training and validation sets. The base models are trained on the training data, and hyper parameters are fine-tuned via cross-validation on the validation set. The stacking and blending techniques combine base model predictions. Stacking uses validation predictions to train a meta-model that intelligently combines individual predictions, while blending directly averages predictions for a simpler ensemble. To overcome the Challenges of privacy preserving, data protection, improve the performance and fast diagnosis, the proposed work is also deployed in a cloud based framework. Performance evaluation employs standard metrics on a separate test set. Results show that the ensemble approach significantly enhances Intracranial Haemorrhage Prediction, improving clinical decision-making for neurological emergencies and fast diagnosis. Our approach incorporates strategies to handle class imbalance, such as using suitable loss functions and data augmentation during training.

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Section: Related Workmentioning

confidence: 99%

Cloud based Stacking and Bending Ensemble Approach for the classification of Brain Haemorrhage using Meta Diverse Model

Seeli,

Thanammal

2024

Preprint

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“…The incidence of ICH itself has tended to decrease, but mortality has still been high during the past decade [ 12 , 34 ]. Approximately 40% of ICH patients die within 30 days after ictus, and only 20% live independently at 6 months [ 1 , 24 , 27 , 38 ]. Therefore, rapid diagnosis and immediate, appropriate treatment are critical.…”

Section: Introductionmentioning

confidence: 99%

“…Differences in regional medical infrastructures have resulted in clear regional disparities in cardiovascular mortality in Korea [ 25 , 29 ]. We previously reported the development of a system entitled “Cloud-based telemedicine platform exclusively for acute ICH patients in rural and underserved areas in Gangwon state” intended to reduce regional disparities in ICH treatment outcomes [ 27 ]. The core of the system is emergent, remote, non-face-to-face consultations with a neurosurgeon for doctors with less ICH expertise at local hospitals in rural and underserved areas.…”

Section: Introductionmentioning

confidence: 99%

“…However, doctors who are not familiar with these guidelines would probably treat these patients in rural and medically underserved area. In addition to the concept of treating ICH patients based on the cloud computing system that we reported previously [ 27 ], we aimed to establish a telemedicine protocol for ICH management exclusively for doctors with less ICH expertise in emergency rooms in rural and medically underserved areas. Four neurosurgeons (J.H.S., Y.K., K.J., and J.J.P.)…”

Section: Introductionmentioning

confidence: 99%

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Telemedicine Protocols for the Management of Patients with Acute Spontaneous Intracerebral Hemorrhage in Rural and Medically Underserved Areas in Gangwon State : Recommendations for Doctors with Less Expertise at Local Emergency Rooms

Jun,

Yang,

Kim

et al. 2024

J Korean Neurosurg Soc

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Previously, we reported the concept of a cloud-based telemedicine platform for patients with intracerebral hemorrhage (ICH) at local emergency rooms in rural and medically underserved areas in Gangwon state by combining artificial intelligence and remote consultation with a neurosurgeon. Developing a telemedicine ICH treatment protocol exclusively for doctors with less ICH expertise working in emergency rooms should be part of establishing this system. Difficulties arise in providing appropriate early treatment for ICH in rural and underserved areas before the patient is transferred to a nearby hub hospital with stroke specialists. This has been an unmet medical need for decade. The available reporting ICH guidelines are realistically possible in university hospitals with a well-equipped infrastructure. However, it is very difficult for doctors inexperienced with ICH treatment to appropriately select and deliver ICH treatment based on the guidelines. To address these issues, we developed an ICH telemedicine protocol. Neurosurgeons from four university hospitals in Gangwon state first wrote the guidelines, and professors with extensive ICH expertise across the country revised them. Guidelines and recommendations for ICH management were described as simply as possible to allow more doctors to use them easily. We hope that our effort in developing the telemedicine protocols will ultimately improve the quality of ICH treatment in local emergency rooms in rural and underserved areas in Gangwon state.

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Computer Vision Algorithms for Surgical Assistance in Cloud-Based Telemedicine

Sharma,

Bordoloi,

Deepak

et al. 2023

2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI)

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Development of Cloud-Based Telemedicine Platform for Acute Intracerebral Hemorrhage in Gangwon-do : Concept and Protocol

Cited by 3 publications

References 22 publications

Cloud based Stacking and Bending Ensemble Approach for the classification of Brain Haemorrhage using Meta Diverse Model

Cloud based Stacking and Bending Ensemble Approach for the classification of Brain Haemorrhage using Meta Diverse Model

Telemedicine Protocols for the Management of Patients with Acute Spontaneous Intracerebral Hemorrhage in Rural and Medically Underserved Areas in Gangwon State : Recommendations for Doctors with Less Expertise at Local Emergency Rooms

Computer Vision Algorithms for Surgical Assistance in Cloud-Based Telemedicine

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