1
|
Wilson SB, Ward J, Munjal V, Lam CSA, Patel M, Zhang P, Xu DS, Chakravarthy VB. Machine Learning in Spine Oncology: A Narrative Review. Global Spine J 2024:21925682241261342. [PMID: 38860699 DOI: 10.1177/21925682241261342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
STUDY DESIGN Narrative Review. OBJECTIVE Machine learning (ML) is one of the latest advancements in artificial intelligence used in medicine and surgery with the potential to significantly impact the way physicians diagnose, prognose, and treat spine tumors. In the realm of spine oncology, ML is utilized to analyze and interpret medical imaging and classify tumors with incredible accuracy. The authors present a narrative review that specifically addresses the use of machine learning in spine oncology. METHODS This study was conducted in accordance with the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) methodology. A systematic review of the literature in the PubMed, EMBASE, Web of Science, Scopus, and Cochrane Library databases since inception was performed to present all clinical studies with the search terms '[[Machine Learning] OR [Artificial Intelligence]] AND [[Spine Oncology] OR [Spine Cancer]]'. Data included studies that were extracted and included algorithms, training and test size, outcomes reported. Studies were separated based on the type of tumor investigated using the machine learning algorithms into primary, metastatic, both, and intradural. A minimum of 2 independent reviewers conducted the study appraisal, data abstraction, and quality assessments of the studies. RESULTS Forty-five studies met inclusion criteria out of 480 references screened from the initial search results. Studies were grouped by metastatic, primary, and intradural tumors. The majority of ML studies relevant to spine oncology focused on utilizing a mixture of clinical and imaging features to risk stratify mortality and frailty. Overall, these studies showed that ML is a helpful tool in tumor detection, differentiation, segmentation, predicting survival, predicting readmission rates of patients with either primary, metastatic, or intradural spine tumors. CONCLUSION Specialized neural networks and deep learning algorithms have shown to be highly effective at predicting malignant probability and aid in diagnosis. ML algorithms can predict the risk of tumor recurrence or progression based on imaging and clinical features. Additionally, ML can optimize treatment planning, such as predicting radiotherapy dose distribution to the tumor and surrounding normal tissue or in surgical resection planning. It has the potential to significantly enhance the accuracy and efficiency of health care delivery, leading to improved patient outcomes.
Collapse
Affiliation(s)
- Seth B Wilson
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | - Jacob Ward
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | - Vikas Munjal
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | | | - Mayur Patel
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | - Ping Zhang
- Department of Computer Science and Engineering, The Ohio State University College of Engineering, Columbus, OH, USA
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH, USA
| | - David S Xu
- Department of Neurosurgery, The Ohio State University, Columbus, OH, USA
| | | |
Collapse
|
2
|
Salehi MA, Mohammadi S, Harandi H, Zakavi SS, Jahanshahi A, Shahrabi Farahani M, Wu JS. Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:766-777. [PMID: 38343243 PMCID: PMC11031503 DOI: 10.1007/s10278-023-00945-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/04/2023] [Accepted: 10/12/2023] [Indexed: 04/20/2024]
Abstract
We aim to conduct a meta-analysis on studies that evaluated the diagnostic performance of artificial intelligence (AI) algorithms in the detection of primary bone tumors, distinguishing them from other bone lesions, and comparing them with clinician assessment. A systematic search was conducted using a combination of keywords related to bone tumors and AI. After extracting contingency tables from all included studies, we performed a meta-analysis using random-effects model to determine the pooled sensitivity and specificity, accompanied by their respective 95% confidence intervals (CI). Quality assessment was evaluated using a modified version of Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST). The pooled sensitivities for AI algorithms and clinicians on internal validation test sets for detecting bone neoplasms were 84% (95% CI: 79.88) and 76% (95% CI: 64.85), and pooled specificities were 86% (95% CI: 81.90) and 64% (95% CI: 55.72), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 84% (95% CI: 75.90) and 91% (95% CI: 83.96), respectively. The same numbers for clinicians were 85% (95% CI: 73.92) and 94% (95% CI: 89.97), respectively. The sensitivity and specificity for clinicians with AI assistance were 95% (95% CI: 86.98) and 57% (95% CI: 48.66). Caution is needed when interpreting findings due to potential limitations. Further research is needed to bridge this gap in scientific understanding and promote effective implementation for medical practice advancement.
Collapse
Affiliation(s)
- Mohammad Amin Salehi
- School of Medicine, Tehran University of Medical Sciences, Pour Sina St, Keshavarz Blvd, Tehran, 1417613151, Iran
| | - Soheil Mohammadi
- School of Medicine, Tehran University of Medical Sciences, Pour Sina St, Keshavarz Blvd, Tehran, 1417613151, Iran.
| | - Hamid Harandi
- School of Medicine, Tehran University of Medical Sciences, Pour Sina St, Keshavarz Blvd, Tehran, 1417613151, Iran
| | - Seyed Sina Zakavi
- School of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Jahanshahi
- School of Medicine, Guilan University of Medical Sciences, Rasht, Iran
| | | | - Jim S Wu
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, USA
| |
Collapse
|
3
|
Connor CW. Understanding New Machine Learning Architectures: Practical Generative Artificial Intelligence for Anesthesiologists. Anesthesiology 2024; 140:599-609. [PMID: 38349761 PMCID: PMC10868863 DOI: 10.1097/aln.0000000000004841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Recent advances in neural networks have given rise to generative artificial intelligence, systems able to produce fluent responses to natural questions or attractive and even photorealistic images from text prompts. These systems were developed through new network architectures that permit massive computational resources to be applied efficiently to enormous data sets. First, this review examines autoencoder architecture and its derivatives the variational autoencoder and the U-Net in annotating and manipulating images and extracting salience. This architecture will be important for applications like automated x-ray interpretation or real-time highlighting of anatomy in ultrasound images. Second, this article examines the transformer architecture in the interpretation and generation of natural language, as it will be useful in producing automated summarization of medical records or performing initial patient screening. The author also applies the GPT-3.5 algorithm to example questions from the American Board of Anesthesiologists Basic Examination and find that, under surprisingly reasonable conditions, it correctly answers more than half the questions.
Collapse
Affiliation(s)
- Christopher W Connor
- Harvard Medical School, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Boston, Massachusetts; Departments of Physiology and Biophysics, and Biomedical Engineering, Boston University, Boston, Massachusetts; Department of Cardiac Anesthesiology and Intensive Care Medicine, Deutsches Herzzentrum der Charité, Charité Universitätsmedizin, Berlin, Germany
| |
Collapse
|
4
|
Kita K, Fujimori T, Suzuki Y, Kanie Y, Takenaka S, Kaito T, Taki T, Ukon Y, Furuya M, Saiwai H, Nakajima N, Sugiura T, Ishiguro H, Kamatani T, Tsukazaki H, Sakai Y, Takami H, Tateiwa D, Hashimoto K, Wataya T, Nishigaki D, Sato J, Hoshiyama M, Tomiyama N, Okada S, Kido S. Bimodal artificial intelligence using TabNet for differentiating spinal cord tumors-Integration of patient background information and images. iScience 2023; 26:107900. [PMID: 37766987 PMCID: PMC10520519 DOI: 10.1016/j.isci.2023.107900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/18/2023] [Accepted: 09/08/2023] [Indexed: 09/29/2023] Open
Abstract
We proposed a bimodal artificial intelligence that integrates patient information with images to diagnose spinal cord tumors. Our model combines TabNet, a state-of-the-art deep learning model for tabular data for patient information, and a convolutional neural network for images. As training data, we collected 259 spinal tumor patients (158 for schwannoma and 101 for meningioma). We compared the performance of the image-only unimodal model, table-only unimodal model, bimodal model using a gradient-boosting decision tree, and bimodal model using TabNet. Our proposed bimodal model using TabNet performed best (area under the receiver-operating characteristic curve [AUROC]: 0.91) in the training data and significantly outperformed the physicians' performance. In the external validation using 62 cases from the other two facilities, our bimodal model showed an AUROC of 0.92, proving the robustness of the model. The bimodal analysis using TabNet was effective for differentiating spinal tumors.
Collapse
Affiliation(s)
- Kosuke Kita
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Takahito Fujimori
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Yuki Suzuki
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Yuya Kanie
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Shota Takenaka
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Takashi Kaito
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Takuyu Taki
- Department of Neurosurgery, Iseikai Hospital, Osaka, Osaka, Japan
| | - Yuichiro Ukon
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | | | - Hirokazu Saiwai
- Department of Orthopedic Surgery, Graduate School of Medical Sciences, Kyusyu University, Higashi, Fukuoka, Japan
| | - Nozomu Nakajima
- Japanese Red Cross Society Himeji Hospital, Himeji, Hyogo, Japan
| | - Tsuyoshi Sugiura
- General Incorporated Foundation Sumitomo Hospital, Osaka, Osaka, Japan
| | - Hiroyuki Ishiguro
- National Hospital Organization Osaka National Hospital, Osaka, Osaka, Japan
| | | | | | | | - Haruna Takami
- Osaka International Cancer Institute, Osaka, Osaka, Japan
| | | | | | - Tomohiro Wataya
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Daiki Nishigaki
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Junya Sato
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | | | - Noriyuki Tomiyama
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| | - Seiji Okada
- Osaka University Graduate School of Medicine Department of Orthopaedic Surgery, Suita, Osaka, Japan
| | - Shoji Kido
- Osaka University School of Medicine Graduate School of Medicine Diagnostic and Interventional Radiology, Suita, Osaka, Japan
| |
Collapse
|
5
|
Zhang J, Li Z, Lin H, Xue M, Wang H, Fang Y, Liu S, Huo T, Zhou H, Yang J, Xie Y, Xie M, Lu L, Liu P, Ye Z. Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures. Front Med (Lausanne) 2023; 10:1224489. [PMID: 37663656 PMCID: PMC10471443 DOI: 10.3389/fmed.2023.1224489] [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: 05/17/2023] [Accepted: 08/04/2023] [Indexed: 09/05/2023] Open
Abstract
Objectives To explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method. Methods A total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals. Results The deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group. Conclusion This deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency.
Collapse
Affiliation(s)
- Jiayao Zhang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhimin Li
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Heng Lin
- Department of Orthopedics, Nanzhang People’s Hospital, Nanzhang, China
| | - Mingdi Xue
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Honglin Wang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Fang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Songxiang Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tongtong Huo
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Zhou
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaming Yang
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Xie
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mao Xie
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lin Lu
- Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan, China
| | - Pengran Liu
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Zhewei Ye
- Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| |
Collapse
|
6
|
Kim JK, Choi GS, Kwak SY, Chang MC. Convolutional Neural Network Algorithm Trained with Anteroposterior Radiographs to Diagnose Pre-Collapse Osteonecrosis of the Femoral Head. APPLIED SCIENCES 2022; 12:9606. [DOI: 10.3390/app12199606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
A convolutional neural network (CNN) is a representative deep-learning algorithm that has a significant advantage in image recognition and classification. Using anteroposterior pelvic radiographs as input data, we developed a CNN algorithm to determine the presence of pre-collapse osteonecrosis of the femoral head (ONFH). We developed a CNN algorithm to differentiate between ONFH and normal radiographs. We retrospectively included 305 anteroposterior pelvic radiographs (right hip: pre-collapsed ONFH = 79, normal = 226; left hip: pre-collapsed ONFH = 62, normal = 243) as data samples. Pre-collapsed ONFH was diagnosed using pelvic magnetic resonance imaging data for each patient. Among the 305 cases, 69.8% of the included data samples were randomly selected as the training set, 21.0% were selected as the validation set, and the remaining 9.2% were selected as the test set to evaluate the performance of the developed CNN algorithm. The area under the curve of our developed CNN algorithm on the test data was 0.912 (95% confidence interval, 0.773–1.000) for the right hip and 0.902 (95% confidence interval, 0.747–1.000) for the left hip. We showed that a CNN algorithm trained using pelvic radiographs would help diagnose pre-collapse ONFH.
Collapse
|