1
|
Kinoshita K, Maruyama T, Kobayashi N, Imanishi S, Maruyama M, Ohira G, Endo S, Tochigi T, Kinoshita M, Fukui Y, Kumazu Y, Kita J, Shinohara H, Matsubara H. An artificial intelligence-based nerve recognition model is useful as surgical support technology and as an educational tool in laparoscopic and robot-assisted rectal cancer surgery. Surg Endosc 2024; 38:5394-5404. [PMID: 39073558 PMCID: PMC11362368 DOI: 10.1007/s00464-024-10939-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/17/2024] [Indexed: 07/30/2024]
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to enhance surgical practice by predicting anatomical structures within the surgical field, thereby supporting surgeons' experiences and cognitive skills. Preserving and utilising nerves as critical guiding structures is paramount in rectal cancer surgery. Hence, we developed a deep learning model based on U-Net to automatically segment nerves. METHODS The model performance was evaluated using 60 randomly selected frames, and the Dice and Intersection over Union (IoU) scores were quantitatively assessed by comparing them with ground truth data. Additionally, a questionnaire was administered to five colorectal surgeons to gauge the extent of underdetection, overdetection, and the practical utility of the model in rectal cancer surgery. Furthermore, we conducted an educational assessment of non-colorectal surgeons, trainees, physicians, and medical students. We evaluated their ability to recognise nerves in mesorectal dissection scenes, scored them on a 12-point scale, and examined the score changes before and after exposure to the AI analysis videos. RESULTS The mean Dice and IoU scores for the 60 test frames were 0.442 (range 0.0465-0.639) and 0.292 (range 0.0238-0.469), respectively. The colorectal surgeons revealed an under-detection score of 0.80 (± 0.47), an over-detection score of 0.58 (± 0.41), and a usefulness evaluation score of 3.38 (± 0.43). The nerve recognition scores of non-colorectal surgeons, rotating residents, and medical students significantly improved by simply watching the AI nerve recognition videos for 1 min. Notably, medical students showed a more substantial increase in nerve recognition scores when exposed to AI nerve analysis videos than when exposed to traditional lectures on nerves. CONCLUSIONS In laparoscopic and robot-assisted rectal cancer surgeries, the AI-based nerve recognition model achieved satisfactory recognition levels for expert surgeons and demonstrated effectiveness in educating junior surgeons and medical students on nerve recognition.
Collapse
Affiliation(s)
- Kazuya Kinoshita
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
- Department of General Surgery, Kumagaya General Hospital, Saitama, Japan
| | - Tetsuro Maruyama
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan.
| | | | - Shunsuke Imanishi
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Michihiro Maruyama
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Gaku Ohira
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Satoshi Endo
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Toru Tochigi
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Mayuko Kinoshita
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| | - Yudai Fukui
- Department of Gastroenterological Surgery, Toranomon Hospital, Tokyo, Japan
| | - Yuta Kumazu
- Anaut Inc, Tokyo, Japan
- Department of Surgery, Yokohama City University, Kanagawa, Japan
| | - Junji Kita
- Department of General Surgery, Kumagaya General Hospital, Saitama, Japan
| | - Hisashi Shinohara
- Department of Gastroenterological Surgery, Hyogo College of Medicine, Hyogo, Japan
| | - Hisahiro Matsubara
- Department of Frontier Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan
| |
Collapse
|
2
|
Martelli E, Capoccia L, Di Francesco M, Cavallo E, Pezzulla MG, Giudice G, Bauleo A, Coppola G, Panagrosso M. Current Applications and Future Perspectives of Artificial and Biomimetic Intelligence in Vascular Surgery and Peripheral Artery Disease. Biomimetics (Basel) 2024; 9:465. [PMID: 39194444 DOI: 10.3390/biomimetics9080465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 07/05/2024] [Accepted: 07/24/2024] [Indexed: 08/29/2024] Open
Abstract
Artificial Intelligence (AI) made its first appearance in 1956, and since then it has progressively introduced itself in healthcare systems and patients' information and care. AI functions can be grouped under the following headings: Machine Learning (ML), Deep Learning (DL), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Computer Vision (CV). Biomimetic intelligence (BI) applies the principles of systems of nature to create biological algorithms, such as genetic and neural network, to be used in different scenarios. Chronic limb-threatening ischemia (CLTI) represents the last stage of peripheral artery disease (PAD) and has increased over recent years, together with the rise in prevalence of diabetes and population ageing. Nowadays, AI and BI grant the possibility of developing new diagnostic and treatment solutions in the vascular field, given the possibility of accessing clinical, biological, and imaging data. By assessing the vascular anatomy in every patient, as well as the burden of atherosclerosis, and classifying the level and degree of disease, sizing and planning the best endovascular treatment, defining the perioperative complications risk, integrating experiences and resources between different specialties, identifying latent PAD, thus offering evidence-based solutions and guiding surgeons in the choice of the best surgical technique, AI and BI challenge the role of the physician's experience in PAD treatment.
Collapse
Affiliation(s)
- Eugenio Martelli
- Division of Vascular Surgery, Department of Surgery, S Maria Goretti Hospital, 81100 Latina, Italy
- Department of General and Specialist Surgery, Sapienza University of Rome, 00161 Rome, Italy
- Faculty of Medicine, Saint Camillus International University of Health Sciences, 00131 Rome, Italy
| | - Laura Capoccia
- Division of Vascular and Endovascular Surgery, Department of Cardiovascular Sciences, S. Anna and S. Sebastiano Hospital, 81100 Caserta, Italy
| | - Marco Di Francesco
- Division of Vascular and Endovascular Surgery, Department of Cardiovascular Sciences, S. Anna and S. Sebastiano Hospital, 81100 Caserta, Italy
| | - Eduardo Cavallo
- Division of Vascular and Endovascular Surgery, Department of Cardiovascular Sciences, S. Anna and S. Sebastiano Hospital, 81100 Caserta, Italy
| | - Maria Giulia Pezzulla
- Division of Vascular and Endovascular Surgery, Department of Cardiovascular Sciences, S. Anna and S. Sebastiano Hospital, 81100 Caserta, Italy
| | - Giorgio Giudice
- Division of Vascular and Endovascular Surgery, Department of Cardiovascular Sciences, S. Anna and S. Sebastiano Hospital, 81100 Caserta, Italy
| | - Antonio Bauleo
- Division of Vascular and Endovascular Surgery, Department of Cardiovascular Sciences, S. Anna and S. Sebastiano Hospital, 81100 Caserta, Italy
| | - Giuseppe Coppola
- Division of Vascular and Endovascular Surgery, Department of Cardiovascular Sciences, S. Anna and S. Sebastiano Hospital, 81100 Caserta, Italy
| | - Marco Panagrosso
- Division of Vascular and Endovascular Surgery, Department of Cardiovascular Sciences, S. Anna and S. Sebastiano Hospital, 81100 Caserta, Italy
| |
Collapse
|
3
|
Feng H, Fu Z, Wang Y, Zhang P, Lai H, Zhao J. Automatic segmentation of thrombosed aortic dissection in post-operative CT-angiography images. Med Phys 2022. [PMID: 36542417 DOI: 10.1002/mp.16169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Revised: 11/02/2022] [Accepted: 11/23/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE The thrombus in the false lumen (FL) of aortic dissection (AD) patients is a meaningful indicator to determine aortic remodeling but difficult to measure in clinic. In this study, a novel segmentation strategy based on deep learning was proposed to automatically extract the thrombus in the FL in post-operative computed tomography angiography (CTA) images of AD patients, which provided an efficient and convenient segmentation method with high accuracy. METHODS A two-step segmentation strategy was proposed. Each step contained a convolutional neural network (CNN) to segment the aorta and the thrombus, respectively. In the first step, a CNN was used to obtain the binary segmentation mask of the whole aorta. In the second step, another CNN was introduced to segment the thrombus. The results of the first step were used as additional input to the second step to highlight the aorta in the complex background. Moreover, skip connection attention refinement (SAR) modules were designed and added in the second step to improve the segmentation accuracy of the thrombus details by efficiently using the low-level features. RESULTS The proposed method provided accurate thrombus segmentation results (0.903 ± 0.062 in dice score, 0.828 ± 0.092 in Jaccard index, and 2.209 ± 2.945 in 95% Hausdorff distance), which showed improvement compared to the methods without prior information (0.846 ± 0.085 in dice score) and the method without SAR (0.899 ± 0.060 in dice score). Moreover, the proposed method achieved 0.967 ± 0.029 and 0.948 ± 0.041 in dice score of true lumen (TL) and patent FL (PFL) segmentation, respectively, indicating the excellence of the proposed method in the segmentation task of the overall aorta. CONCLUSIONS A novel CNN-based segmentation framework was proposed to automatically obtain thrombus segmentation for thrombosed AD in post-operative CTA images, which provided a useful tool for further application of thrombus-related indicators in clinical and research application.
Collapse
Affiliation(s)
- Hanying Feng
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Zheng Fu
- Department of Cardiovascular Surgery, Zhongshan Hospital Fudan University, Shanghai, People's Republic of China
| | - Yulin Wang
- Department of Cardiovascular Surgery, Zhongshan Hospital Fudan University, Shanghai, People's Republic of China
| | - Puming Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Hao Lai
- Department of Cardiovascular Surgery, Zhongshan Hospital Fudan University, Shanghai, People's Republic of China
| | - Jun Zhao
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| |
Collapse
|
4
|
Kumazu Y, Kobayashi N, Kitamura N, Rayan E, Neculoiu P, Misumi T, Hojo Y, Nakamura T, Kumamoto T, Kurahashi Y, Ishida Y, Masuda M, Shinohara H. Automated segmentation by deep learning of loose connective tissue fibers to define safe dissection planes in robot-assisted gastrectomy. Sci Rep 2021; 11:21198. [PMID: 34707141 PMCID: PMC8551298 DOI: 10.1038/s41598-021-00557-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 10/13/2021] [Indexed: 02/06/2023] Open
Abstract
The prediction of anatomical structures within the surgical field by artificial intelligence (AI) is expected to support surgeons’ experience and cognitive skills. We aimed to develop a deep-learning model to automatically segment loose connective tissue fibers (LCTFs) that define a safe dissection plane. The annotation was performed on video frames capturing a robot-assisted gastrectomy performed by trained surgeons. A deep-learning model based on U-net was developed to output segmentation results. Twenty randomly sampled frames were provided to evaluate model performance by comparing Recall and F1/Dice scores with a ground truth and with a two-item questionnaire on sensitivity and misrecognition that was completed by 20 surgeons. The model produced high Recall scores (mean 0.606, maximum 0.861). Mean F1/Dice scores reached 0.549 (range 0.335–0.691), showing acceptable spatial overlap of the objects. Surgeon evaluators gave a mean sensitivity score of 3.52 (with 88.0% assigning the highest score of 4; range 2.45–3.95). The mean misrecognition score was a low 0.14 (range 0–0.7), indicating very few acknowledged over-detection failures. Thus, AI can be trained to predict fine, difficult-to-discern anatomical structures at a level convincing to expert surgeons. This technology may help reduce adverse events by determining safe dissection planes.
Collapse
Affiliation(s)
- Yuta Kumazu
- Department of Surgery, Yokohama City University, Kanagawa, Japan.,Anaut Inc., Tokyo, Japan
| | | | | | | | | | - Toshihiro Misumi
- Department of Biostatistics, Yokohama City University School of Medicine, Kanagawa, Japan
| | - Yudai Hojo
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Tatsuro Nakamura
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Tsutomu Kumamoto
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Yasunori Kurahashi
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Yoshinori Ishida
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan
| | - Munetaka Masuda
- Department of Surgery, Yokohama City University, Kanagawa, Japan
| | - Hisashi Shinohara
- Department of Gastroenterological Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan.
| |
Collapse
|
5
|
Chlap P, Min H, Vandenberg N, Dowling J, Holloway L, Haworth A. A review of medical image data augmentation techniques for deep learning applications. J Med Imaging Radiat Oncol 2021; 65:545-563. [PMID: 34145766 DOI: 10.1111/1754-9485.13261] [Citation(s) in RCA: 174] [Impact Index Per Article: 58.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 05/23/2021] [Indexed: 12/21/2022]
Abstract
Research in artificial intelligence for radiology and radiotherapy has recently become increasingly reliant on the use of deep learning-based algorithms. While the performance of the models which these algorithms produce can significantly outperform more traditional machine learning methods, they do rely on larger datasets being available for training. To address this issue, data augmentation has become a popular method for increasing the size of a training dataset, particularly in fields where large datasets aren't typically available, which is often the case when working with medical images. Data augmentation aims to generate additional data which is used to train the model and has been shown to improve performance when validated on a separate unseen dataset. This approach has become commonplace so to help understand the types of data augmentation techniques used in state-of-the-art deep learning models, we conducted a systematic review of the literature where data augmentation was utilised on medical images (limited to CT and MRI) to train a deep learning model. Articles were categorised into basic, deformable, deep learning or other data augmentation techniques. As artificial intelligence models trained using augmented data make their way into the clinic, this review aims to give an insight to these techniques and confidence in the validity of the models produced.
Collapse
Affiliation(s)
- Phillip Chlap
- South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Sydney, New South Wales, Australia
| | - Hang Min
- South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,The Australian e-Health and Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia
| | - Nym Vandenberg
- Institute of Medical Physics, University of Sydney, Sydney, New South Wales, Australia
| | - Jason Dowling
- South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,The Australian e-Health and Research Centre, CSIRO Health and Biosecurity, Brisbane, Queensland, Australia
| | - Lois Holloway
- South Western Sydney Clinical School, University of New South Wales, Sydney, New South Wales, Australia.,Ingham Institute for Applied Medical Research, Sydney, New South Wales, Australia.,Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Sydney, New South Wales, Australia.,Institute of Medical Physics, University of Sydney, Sydney, New South Wales, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, New South Wales, Australia
| | - Annette Haworth
- Institute of Medical Physics, University of Sydney, Sydney, New South Wales, Australia
| |
Collapse
|
6
|
Flores AM, Demsas F, Leeper NJ, Ross EG. Leveraging Machine Learning and Artificial Intelligence to Improve Peripheral Artery Disease Detection, Treatment, and Outcomes. Circ Res 2021; 128:1833-1850. [PMID: 34110911 PMCID: PMC8285054 DOI: 10.1161/circresaha.121.318224] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Peripheral artery disease is an atherosclerotic disorder which, when present, portends poor patient outcomes. Low diagnosis rates perpetuate poor management, leading to limb loss and excess rates of cardiovascular morbidity and death. Machine learning algorithms and artificially intelligent systems have shown great promise in application to many areas in health care, such as accurately detecting disease, predicting patient outcomes, and automating image interpretation. Although the application of these technologies to peripheral artery disease are in their infancy, their promises are tremendous. In this review, we provide an introduction to important concepts in the fields of machine learning and artificial intelligence, detail the current state of how these technologies have been applied to peripheral artery disease, and discuss potential areas for future care enhancement with advanced analytics.
Collapse
Affiliation(s)
- Alyssa M Flores
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
| | - Falen Demsas
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
| | - Nicholas J Leeper
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
- Department of Medicine, Division of Cardiovascular Medicine (N.J.L.), Stanford University School of Medicine, CA
- Stanford Cardiovascular Institute, CA (N.J.L., E.G.R.)
| | - Elsie Gyang Ross
- Department of Surgery, Division of Vascular Surgery (A.M.F., F.D., N.J.L., E.G.R.), Stanford University School of Medicine, CA
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University, CA. (E.G.R.)
- Stanford Cardiovascular Institute, CA (N.J.L., E.G.R.)
| |
Collapse
|
7
|
Bell M, Gandhi R, Shawer H, Tsoumpas C, Bailey MA. Imaging Biological Pathways in Abdominal Aortic Aneurysms Using Positron Emission Tomography. Arterioscler Thromb Vasc Biol 2021; 41:1596-1606. [PMID: 33761759 DOI: 10.1161/atvbaha.120.315812] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
[Figure: see text].
Collapse
Affiliation(s)
- Michael Bell
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, United Kingdom
| | - Richa Gandhi
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, United Kingdom
| | - Heba Shawer
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, United Kingdom
| | - Charalampos Tsoumpas
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, United Kingdom
| | - Marc A Bailey
- Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, United Kingdom
| |
Collapse
|