Deep learning‐based intraoperative video analysis for supporting surgery

Bai, J.

doi:10.1002/cpe.7837

Cited by 2 publications

(2 citation statements)

References 83 publications

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“…The proposed methods [4][5][6][7][8] in previous studies show good performance for detecting and classifying surgical tools. However, the recognition phase of the surgery being carried out is still considered a challenging task [6].…”

Section: Related Workmentioning

confidence: 79%

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A Multimodal Transformer Model for Recognition of Images from Complex Laparoscopic Surgical Videos

Abiyev,

Altabel,

Darwish

et al. 2024

Diagnostics

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The determination of the potential role and advantages of artificial intelligence-based models in the field of surgery remains uncertain. This research marks an initial stride towards creating a multimodal model, inspired by the Video-Audio-Text Transformer, that aims to reduce negative occurrences and enhance patient safety. The model employs text and image embedding state-of-the-art models (ViT and BERT) to assess their efficacy in extracting the hidden and distinct features from the surgery video frames. These features are then used as inputs for convolution-free Transformer architectures to extract comprehensive multidimensional representations. A joint space is then used to combine the text and image features extracted from both Transformer encoders. This joint space ensures that the relationships between the different modalities are preserved during the combination process. The entire model was trained and tested on laparoscopic cholecystectomy (LC) videos encompassing various levels of complexity. Experimentally, a mean accuracy of 91.0%, a precision of 81%, and a recall of 83% were reached by the model when tested on 30 videos out of 80 from the Cholec80 dataset.

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Section: Related Workmentioning

confidence: 79%

“…The recognition of surgical activities and the conceptualization of surgical workflow are heavily dependent on accurate surgical tool detection [1,3]. Various techniques and methodologies have been employed to identify surgical tools [4,5]. Early approaches used radio-frequency identification (RFID) systems to acquire tool use signals [5].…”

Section: Introductionmentioning

confidence: 99%

A Multimodal Transformer Model for Recognition of Images from Complex Laparoscopic Surgical Videos

Abiyev,

Altabel,

Darwish

et al. 2024

Diagnostics

View full text Add to dashboard Cite

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A Soft Voting Ensemble Model for Hotel Revenue Prediction

Jiang,

Ni,

Chen

2024

IJEFM

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In recent years, the hotel industry has faced unprecedented opportunities and challenges due to the increasing demand for travel and business trips. This growth not only presents significant opportunities but also brings challenges to resource management and price setting. Accurate hotel revenue prediction is crucial for the hotel industry as it influences pricing strategies and resource allocation. However, traditional hotel revenue prediction models fail to capture the diversity and complexity of hotel revenue data, resulting in inefficient and inaccurate predictions. Then, with the development of the ensemble learning, its application to hotel revenue prediction has emerged as an influential research direction. This study proposes a soft voting ensemble model for hotel revenue prediction, which includes six base models: Convolutional Neural Network, K-nearest Neighbors, Linear Regression, Long Short-term Memory, Multi-layer Perceptron, and Recurrent Neural Network. Firstly, the hyper-parameters of the base models are optimized with Bayesian optimization. Subsequently, a soft voting ensemble method is used to aggregate the predictions of each base model. Finally, experimental results on the hotel revenue dataset demonstrate that the soft voting ensemble model outperforms base models across six key performance metrics, providing hotel managers with more accurate revenue prediction tools to aid in scientific management decisions and resource allocation strategies. This study confirms the effectiveness of the soft voting ensemble model in enhancing the accuracy of hotel revenue forecasts, demonstrating its significant potential for application in strategic planning within the modern hotel industry.

show abstract

Deep learning‐based intraoperative video analysis for supporting surgery

Cited by 2 publications

References 83 publications

A Multimodal Transformer Model for Recognition of Images from Complex Laparoscopic Surgical Videos

A Multimodal Transformer Model for Recognition of Images from Complex Laparoscopic Surgical Videos

A Soft Voting Ensemble Model for Hotel Revenue Prediction

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