Research

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14 pages, 2872 KiB

Open AccessArticle

Localization of Colorectal Cancer Lesions in Contrast-Computed Tomography Images via a Deep Learning Approach

by Prasan Kumar Sahoo, Pushpanjali Gupta, Ying-Chieh Lai, Sum-Fu Chiang, Jeng-Fu You, Djeane Debora Onthoni and Yih-Jong Chern

Bioengineering 2023, 10(8), 972; https://doi.org/10.3390/bioengineering10080972 - 17 Aug 2023

Cited by 1 | Viewed by 2313

Abstract

Abdominal computed tomography (CT) is a frequently used imaging modality for evaluating gastrointestinal diseases. The detection of colorectal cancer is often realized using CT before a more invasive colonoscopy. When a CT exam is performed for indications other than colorectal evaluation, the tortuous [...] Read more.

Abdominal computed tomography (CT) is a frequently used imaging modality for evaluating gastrointestinal diseases. The detection of colorectal cancer is often realized using CT before a more invasive colonoscopy. When a CT exam is performed for indications other than colorectal evaluation, the tortuous structure of the long, tubular colon makes it difficult to analyze the colon carefully and thoroughly. In addition, the sensitivity of CT in detecting colorectal cancer is greatly dependent on the size of the tumor. Missed incidental colon cancers using CT are an emerging problem for clinicians and radiologists; consequently, the automatic localization of lesions in the CT images of unprepared bowels is needed. Therefore, this study used artificial intelligence (AI) to localize colorectal cancer in CT images. We enrolled 190 colorectal cancer patients to obtain 1558 tumor slices annotated by radiologists and colorectal surgeons. The tumor sites were double-confirmed via colonoscopy or other related examinations, including physical examination or image study, and the final tumor sites were obtained from the operation records if available. The localization and training models used were RetinaNet, YOLOv3, and YOLOv8. We achieved an F1 score of 0.97 (±0.002), a mAP of 0.984 when performing slice-wise testing, 0.83 (±0.29) sensitivity, 0.97 (±0.01) specificity, and 0.96 (±0.01) accuracy when performing patient-wise testing using our derived model YOLOv8 with hyperparameter tuning. Full article

(This article belongs to the Special Issue Deep Learning and Medical Innovation in Minimally Invasive Surgery)

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12 pages, 8726 KiB

Open AccessArticle

Deep Learning-Based Automated Magnetic Resonance Image Segmentation of the Lumbar Structure and Its Adjacent Structures at the L4/5 Level

by Min Wang, Zhihai Su, Zheng Liu, Tao Chen, Zhifei Cui, Shaolin Li, Shumao Pang and Hai Lu

Bioengineering 2023, 10(8), 963; https://doi.org/10.3390/bioengineering10080963 - 15 Aug 2023

Cited by 1 | Viewed by 922

Abstract

(1) Background: This study aims to develop a deep learning model based on a 3D Deeplab V3+ network to automatically segment multiple structures from magnetic resonance (MR) images at the L4/5 level. (2) Methods: After data preprocessing, the modified 3D Deeplab V3+ network [...] Read more.

(1) Background: This study aims to develop a deep learning model based on a 3D Deeplab V3+ network to automatically segment multiple structures from magnetic resonance (MR) images at the L4/5 level. (2) Methods: After data preprocessing, the modified 3D Deeplab V3+ network of the deep learning model was used for the automatic segmentation of multiple structures from MR images at the L4/5 level. We performed five-fold cross-validation to evaluate the performance of the deep learning model. Subsequently, the Dice Similarity Coefficient (DSC), precision, and recall were also used to assess the deep learning model’s performance. Pearson’s correlation coefficient analysis and the Wilcoxon signed-rank test were employed to compare the morphometric measurements of 3D reconstruction models generated by manual and automatic segmentation. (3) Results: The deep learning model obtained an overall average DSC of 0.886, an average precision of 0.899, and an average recall of 0.881 on the test sets. Furthermore, all morphometry-related measurements of 3D reconstruction models revealed no significant difference between ground truth and automatic segmentation. Strong linear relationships and correlations were also obtained in the morphometry-related measurements of 3D reconstruction models between ground truth and automated segmentation. (4) Conclusions: We found it feasible to perform automated segmentation of multiple structures from MR images, which would facilitate lumbar surgical evaluation by establishing 3D reconstruction models at the L4/5 level. Full article

(This article belongs to the Special Issue Deep Learning and Medical Innovation in Minimally Invasive Surgery)

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12 pages, 1625 KiB

Open AccessArticle

The Application of Design Thinking in Developing a Deep Learning Algorithm for Hip Fracture Detection

by Chun-Hsiang Ouyang, Chih-Chi Chen, Yu-San Tee, Wei-Cheng Lin, Ling-Wei Kuo, Chien-An Liao, Chi-Tung Cheng and Chien-Hung Liao

Bioengineering 2023, 10(6), 735; https://doi.org/10.3390/bioengineering10060735 - 19 Jun 2023

Cited by 1 | Viewed by 1411

Abstract

(1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to use [...] Read more.

(1) Background: Design thinking is a problem-solving approach that has been applied in various sectors, including healthcare and medical education. While deep learning (DL) algorithms can assist in clinical practice, integrating them into clinical scenarios can be challenging. This study aimed to use design thinking steps to develop a DL algorithm that accelerates deployment in clinical practice and improves its performance to meet clinical requirements. (2) Methods: We applied the design thinking process to interview clinical doctors and gain insights to develop and modify the DL algorithm to meet clinical scenarios. We also compared the DL performance of the algorithm before and after the integration of design thinking. (3) Results: After empathizing with clinical doctors and defining their needs, we identified the unmet need of five trauma surgeons as “how to reduce the misdiagnosis of femoral fracture by pelvic plain film (PXR) at initial emergency visiting”. We collected 4235 PXRs from our hospital, of which 2146 had a hip fracture (51%) from 2008 to 2016. We developed hip fracture DL detection models based on the Xception convolutional neural network by using these images. By incorporating design thinking, we improved the diagnostic accuracy from 0.91 (0.84–0.96) to 0.95 (0.93–0.97), the sensitivity from 0.97 (0.89–1.00) to 0.97 (0.94–0.99), and the specificity from 0.84 (0.71–0.93) to 0.93(0.990–0.97). (4) Conclusions: In summary, this study demonstrates that design thinking can ensure that DL solutions developed for trauma care are user-centered and meet the needs of patients and healthcare providers. Full article

(This article belongs to the Special Issue Deep Learning and Medical Innovation in Minimally Invasive Surgery)

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15 pages, 41119 KiB

Open AccessEditor’s ChoiceArticle

Establishment of Surgical Difficulty Grading System and Application of MRI-Based Artificial Intelligence to Stratify Difficulty in Laparoscopic Rectal Surgery

by Zhen Sun, Wenyun Hou, Weimin Liu, Jingjuan Liu, Kexuan Li, Bin Wu, Guole Lin, Huadan Xue, Junjun Pan and Yi Xiao

Bioengineering 2023, 10(4), 468; https://doi.org/10.3390/bioengineering10040468 - 12 Apr 2023

Cited by 3 | Viewed by 1592

Abstract

(1) Background: The difficulty of pelvic operation is greatly affected by anatomical constraints. Defining this difficulty and assessing it based on conventional methods has some limitations. Artificial intelligence (AI) has enabled rapid advances in surgery, but its role in assessing the difficulty of [...] Read more.

(1) Background: The difficulty of pelvic operation is greatly affected by anatomical constraints. Defining this difficulty and assessing it based on conventional methods has some limitations. Artificial intelligence (AI) has enabled rapid advances in surgery, but its role in assessing the difficulty of laparoscopic rectal surgery is unclear. This study aimed to establish a difficulty grading system to assess the difficulty of laparoscopic rectal surgery, as well as utilize this system to evaluate the reliability of pelvis-induced difficulties described by MRI-based AI. (2) Methods: Patients who underwent laparoscopic rectal surgery from March 2019 to October 2022 were included, and were divided into a non-difficult group and difficult group. This study was divided into two stages. In the first stage, a difficulty grading system was developed and proposed to assess the surgical difficulty caused by the pelvis. In the second stage, AI was used to build a model, and the ability of the model to stratify the difficulty of surgery was evaluated at this stage, based on the results of the first stage; (3) Results: Among the 108 enrolled patients, 53 patients (49.1%) were in the difficult group. Compared to the non-difficult group, there were longer operation times, more blood loss, higher rates of anastomotic leaks, and poorer specimen quality in the difficult group. In the second stage, after training and testing, the average accuracy of the four-fold cross validation models on the test set was 0.830, and the accuracy of the merged AI model was 0.800, the precision was 0.786, the specificity was 0.750, the recall was 0.846, the F1-score was 0.815, the area under the receiver operating curve was 0.78 and the average precision was 0.69; (4) Conclusions: This study successfully proposed a feasible grading system for surgery difficulty and developed a predictive model with reasonable accuracy using AI, which can assist surgeons in determining surgical difficulty and in choosing the optimal surgical approach for rectal cancer patients with a structurally difficult pelvis. Full article

(This article belongs to the Special Issue Deep Learning and Medical Innovation in Minimally Invasive Surgery)

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11 pages, 900 KiB

Open AccessArticle

The Feasibility and Performance of Total Hip Replacement Prediction Deep Learning Algorithm with Real World Data

by Chih-Chi Chen, Jen-Fu Huang, Wei-Cheng Lin, Chi-Tung Cheng, Shann-Ching Chen, Chih-Yuan Fu, Mel S. Lee, Chien-Hung Liao and Chia-Ying Chung

Bioengineering 2023, 10(4), 458; https://doi.org/10.3390/bioengineering10040458 - 09 Apr 2023

Cited by 1 | Viewed by 1311

Abstract

(1) Background: Hip degenerative disorder is a common geriatric disease is the main causes to lead to total hip replacement (THR). The surgical timing of THR is crucial for post-operative recovery. Deep learning (DL) algorithms can be used to detect anomalies in medical [...] Read more.

(1) Background: Hip degenerative disorder is a common geriatric disease is the main causes to lead to total hip replacement (THR). The surgical timing of THR is crucial for post-operative recovery. Deep learning (DL) algorithms can be used to detect anomalies in medical images and predict the need for THR. The real world data (RWD) were used to validate the artificial intelligence and DL algorithm in medicine but there was no previous study to prove its function in THR prediction. (2) Methods: We designed a sequential two-stage hip replacement prediction deep learning algorithm to identify the possibility of THR in three months of hip joints by plain pelvic radiography (PXR). We also collected RWD to validate the performance of this algorithm. (3) Results: The RWD totally included 3766 PXRs from 2018 to 2019. The overall accuracy of the algorithm was 0.9633; sensitivity was 0.9450; specificity was 1.000 and the precision was 1.000. The negative predictive value was 0.9009, the false negative rate was 0.0550, and the F1 score was 0.9717. The area under curve was 0.972 with 95% confidence interval from 0.953 to 0.987. (4) Conclusions: In summary, this DL algorithm can provide an accurate and reliable method for detecting hip degeneration and predicting the need for further THR. RWD offered an alternative support of the algorithm and validated its function to save time and cost. Full article

(This article belongs to the Special Issue Deep Learning and Medical Innovation in Minimally Invasive Surgery)

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Review

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18 pages, 1405 KiB

Open AccessReview

Deep Learning in Barrett’s Esophagus Diagnosis: Current Status and Future Directions

by Ruichen Cui, Lei Wang, Lin Lin, Jie Li, Runda Lu, Shixiang Liu, Bowei Liu, Yimin Gu, Hanlu Zhang, Qixin Shang, Longqi Chen and Dong Tian

Bioengineering 2023, 10(11), 1239; https://doi.org/10.3390/bioengineering10111239 - 24 Oct 2023

Viewed by 1136

Abstract

Barrett’s esophagus (BE) represents a pre-malignant condition characterized by abnormal cellular proliferation in the distal esophagus. A timely and accurate diagnosis of BE is imperative to prevent its progression to esophageal adenocarcinoma, a malignancy associated with a significantly reduced survival rate. In this [...] Read more.

Barrett’s esophagus (BE) represents a pre-malignant condition characterized by abnormal cellular proliferation in the distal esophagus. A timely and accurate diagnosis of BE is imperative to prevent its progression to esophageal adenocarcinoma, a malignancy associated with a significantly reduced survival rate. In this digital age, deep learning (DL) has emerged as a powerful tool for medical image analysis and diagnostic applications, showcasing vast potential across various medical disciplines. In this comprehensive review, we meticulously assess 33 primary studies employing varied DL techniques, predominantly featuring convolutional neural networks (CNNs), for the diagnosis and understanding of BE. Our primary focus revolves around evaluating the current applications of DL in BE diagnosis, encompassing tasks such as image segmentation and classification, as well as their potential impact and implications in real-world clinical settings. While the applications of DL in BE diagnosis exhibit promising results, they are not without challenges, such as dataset issues and the “black box” nature of models. We discuss these challenges in the concluding section. Essentially, while DL holds tremendous potential to revolutionize BE diagnosis, addressing these challenges is paramount to harnessing its full capacity and ensuring its widespread application in clinical practice. Full article

(This article belongs to the Special Issue Deep Learning and Medical Innovation in Minimally Invasive Surgery)

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13 pages, 676 KiB

Open AccessReview

Using AI to Detect Pain through Facial Expressions: A Review

by Gioacchino D. De Sario, Clifton R. Haider, Karla C. Maita, Ricardo A. Torres-Guzman, Omar S. Emam, Francisco R. Avila, John P. Garcia, Sahar Borna, Christopher J. McLeod, Charles J. Bruce, Rickey E. Carter and Antonio J. Forte

Bioengineering 2023, 10(5), 548; https://doi.org/10.3390/bioengineering10050548 - 02 May 2023

Cited by 4 | Viewed by 3096

Abstract

Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical [...] Read more.

Pain assessment is a complex task largely dependent on the patient’s self-report. Artificial intelligence (AI) has emerged as a promising tool for automating and objectifying pain assessment through the identification of pain-related facial expressions. However, the capabilities and potential of AI in clinical settings are still largely unknown to many medical professionals. In this literature review, we present a conceptual understanding of the application of AI to detect pain through facial expressions. We provide an overview of the current state of the art as well as the technical foundations of AI/ML techniques used in pain detection. We highlight the ethical challenges and the limitations associated with the use of AI in pain detection, such as the scarcity of databases, confounding factors, and medical conditions that affect the shape and mobility of the face. The review also highlights the potential impact of AI on pain assessment in clinical practice and lays the groundwork for further study in this area. Full article

(This article belongs to the Special Issue Deep Learning and Medical Innovation in Minimally Invasive Surgery)

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15 pages, 632 KiB

Open AccessReview

A Review of Voice-Based Pain Detection in Adults Using Artificial Intelligence

by Sahar Borna, Clifton R. Haider, Karla C. Maita, Ricardo A. Torres, Francisco R. Avila, John P. Garcia, Gioacchino D. De Sario Velasquez, Christopher J. McLeod, Charles J. Bruce, Rickey E. Carter and Antonio J. Forte

Bioengineering 2023, 10(4), 500; https://doi.org/10.3390/bioengineering10040500 - 21 Apr 2023

Cited by 2 | Viewed by 2234

Abstract

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared [...] Read more.

Pain is a complex and subjective experience, and traditional methods of pain assessment can be limited by factors such as self-report bias and observer variability. Voice is frequently used to evaluate pain, occasionally in conjunction with other behaviors such as facial gestures. Compared to facial emotions, there is less available evidence linking pain with voice. This literature review synthesizes the current state of research on the use of voice recognition and voice analysis for pain detection in adults, with a specific focus on the role of artificial intelligence (AI) and machine learning (ML) techniques. We describe the previous works on pain recognition using voice and highlight the different approaches to voice as a tool for pain detection, such as a human effect or biosignal. Overall, studies have shown that AI-based voice analysis can be an effective tool for pain detection in adult patients with various types of pain, including chronic and acute pain. We highlight the high accuracy of the ML-based approaches used in studies and their limitations in terms of generalizability due to factors such as the nature of the pain and patient population characteristics. However, there are still potential challenges, such as the need for large datasets and the risk of bias in training models, which warrant further research. Full article

(This article belongs to the Special Issue Deep Learning and Medical Innovation in Minimally Invasive Surgery)

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