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Nguyen TP, Kim JH, Kim SH, Yoon J, Choi SH. Machine Learning-Based Measurement of Regional and Global Spinal Parameters Using the Concept of Incidence Angle of Inflection Points. Bioengineering (Basel) 2023; 10:1236. [PMID: 37892966 PMCID: PMC10604057 DOI: 10.3390/bioengineering10101236] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 10/05/2023] [Accepted: 10/18/2023] [Indexed: 10/29/2023] Open
Abstract
This study delves into the application of convolutional neural networks (CNNs) in evaluating spinal sagittal alignment, introducing the innovative concept of incidence angles of inflection points (IAIPs) as intuitive parameters to capture the interplay between pelvic and spinal alignment. Pioneering the fusion of IAIPs with machine learning for sagittal alignment analysis, this research scrutinized whole-spine lateral radiographs from hundreds of patients who visited a single institution, utilizing high-quality images for parameter assessments. Noteworthy findings revealed robust success rates for certain parameters, including pelvic and C2 incidence angles, but comparatively lower rates for sacral slope and L1 incidence. The proposed CNN-based machine learning method demonstrated remarkable efficiency, achieving an impressive 80 percent detection rate for various spinal angles, such as lumbar lordosis and thoracic kyphosis, with a precise error threshold of 3.5°. Further bolstering the study's credibility, measurements derived from the novel formula closely aligned with those directly extracted from the CNN model. In conclusion, this research underscores the utility of the CNN-based deep learning algorithm in delivering precise measurements of spinal sagittal parameters, and highlights the potential for integrating machine learning with the IAIP concept for comprehensive data accumulation in the domain of sagittal spinal alignment analysis, thus advancing our understanding of spinal health.
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Affiliation(s)
- Thong Phi Nguyen
- Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea
- Department of Mechanical Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea
| | - Ji-Hwan Kim
- Department of Orthopedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Seong-Ha Kim
- Department of Orthopedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Jonghun Yoon
- Department of Mechanical Engineering, BK21 FOUR ERICA-ACE Center, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea
- Department of Mechanical Engineering, Hanyang University, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea
- AIDICOME Inc., 221, 5th Engineering Building, 55 Hanyangdaehak-ro, Sangnok-gu, Ansan-si 15588, Gyeonggi-do, Republic of Korea
| | - Sung-Hoon Choi
- Department of Orthopedic Surgery, Hanyang University College of Medicine, 222 Wangsimni-ro, Seongdong-gu, Seoul 04763, Republic of Korea
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Nguyen TP, Chae DS, Choi SH, Jeong K, Yoon J. Enhancement of Hip X-ray with Convolutional Autoencoder for Increasing Prediction Accuracy of Bone Mineral Density. Bioengineering (Basel) 2023; 10:1169. [PMID: 37892899 PMCID: PMC10604653 DOI: 10.3390/bioengineering10101169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 10/29/2023] Open
Abstract
It is very important to keep track of decreases in the bone mineral density (BMD) of elderly people since it can be correlated with the risk of incidence of major osteoporotic fractures leading to fatal injuries. Even though dual-energy X-ray absorptiometry (DXA) is the one of the most precise measuring techniques used to quantify BMD, most patients have restricted access to this machine due to high cost of DXA equipment, which is also rarely distributed to local clinics. Meanwhile, the conventional X-rays, which are commonly used for visualizing conditions and injuries due to their low cost, combine the absorption of both soft and bone tissues, consequently limiting its ability to measure BMD. Therefore, we have proposed a specialized automated smart system to quantitatively predict BMD based on a conventional X-ray image only by reducing the soft tissue effect supported by the implementation of a convolutional autoencoder, which is trained using proposed synthesized data to generate grayscale values of bone tissue alone. From the enhanced image, multiple features are calculated from the hip X-ray to predict the BMD values. The performance of the proposed method has been validated through comparison with the DXA value, which shows high consistency with correlation coefficient of 0.81 and mean absolute error of 0.069 g/cm2.
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Affiliation(s)
- Thong Phi Nguyen
- Department of Mechanical Design Engineering, Hanyang University, Seoul 04763, Republic of Korea; (T.P.N.); (K.J.)
- BK21 FOUR ERICA-ACE Center, Hanyang University, Ansan 15588, Republic of Korea
| | - Dong-Sik Chae
- Department of Orthopedic Surgery, International St. Mary’s Hospital, Catholic Kwandong University College of Medicine, Incheon 22711, Republic of Korea;
| | - Sung Hoon Choi
- Department of Orthopaedic Surgery, Hanyang University College of Medicine, Seoul 04763, Republic of Korea;
| | - Kyucheol Jeong
- Department of Mechanical Design Engineering, Hanyang University, Seoul 04763, Republic of Korea; (T.P.N.); (K.J.)
- BK21 FOUR ERICA-ACE Center, Hanyang University, Ansan 15588, Republic of Korea
| | - Jonghun Yoon
- BK21 FOUR ERICA-ACE Center, Hanyang University, Ansan 15588, Republic of Korea
- Department of Mechanical Engineering, Hanyang University, Ansan 15588, Republic of Korea
- AIDICOME Inc., Ansan 15588, Republic of Korea
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Ahn J, Nguyen TP, Kim YJ, Kim T, Yoon J. Automated analysis of three-dimensional CBCT images taken in natural head position that combines facial profile processing and multiple deep-learning models. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107123. [PMID: 36156440 DOI: 10.1016/j.cmpb.2022.107123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 08/24/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Analyzing three-dimensional cone beam computed tomography (CBCT) images has become an indispensable procedure for diagnosis and treatment planning of orthodontic patients. Artificial intelligence, especially deep-learning techniques for analyzing image data, shows great potential for medical and dental image analysis and diagnosis. To explore the feasibility of automating measurement of 13 geometric parameters from three-dimensional cone beam computed tomography images taken in natural head position (NHP), this study proposed a smart system that combined a facial profile analysis algorithm with deep-learning models. MATERIALS AND METHODS Using multiple views extracted from the cone beam computed tomography data of 170 cases as a dataset, our proposed method automatically calculated 13 dental parameters by partitioning, detecting regions of interest, and extracting the facial profile. Subsequently, Mask-RCNN, a trained decentralized convolutional neural network was applied to detect 23 landmarks. All the techniques were integrated into a software application with a graphical user interface designed for user convenience. To demonstrate the system's ability to replace human experts, 30 CBCT data were selected for validation. Two orthodontists and one advanced general dentist located required landmarks by using a commercial dental program. The differences between manual and developed methods were calculated and reported as the errors. RESULTS The intraclass correlation coefficients (ICCs) and 95% confidence interval (95% CI) for intra-observer reliability were 0.98 (0.97-0.99) for observer 1; 0.95 (0.93-0.97) for observer 2; 0.98 (0.97-0.99) for observer 3 after measuring 13 parameters two times at two weeks interval. The combined ICC for intra-observer reliability was 0.97. The ICCs and 95% CI for inter-observer reliability were 0.94 (0.91-0.97). The mean absolute value of deviation was around 1 mm for the length parameters, and smaller than 2° for angle parameters. Furthermore, ANOVA test demonstrated the consistency between the measurements of the proposed method and those of human experts statistically (Fdis=2.68, ɑ=0.05). CONCLUSIONS The proposed system demonstrated the high consistency with the manual measurements of human experts and its applicability. This method aimed to help human experts save time and efforts for analyzing three-dimensional CBCT images of orthodontic patients.
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Affiliation(s)
- Janghoon Ahn
- Department of Orthodontics, Kangnam Sacred Heart Hospital, Hallym University, Singil-ro 1 gil, Yeongdeungpo-gu, Seoul 07441, Republic of Korea
| | - Thong Phi Nguyen
- Department of Mechanical Design Engineering/ Major in Materials, Devices, and Equipment, Hanyang University, 222, Wangsimni-ro, Seongdongsu, Seoul 04763, Republic of Korea; BK21 FOUR ERICA-ACE Centre, Hanyang University, Ansan-si, Gyeonggi-do 15588, Republic of Korea
| | - Yoon-Ji Kim
- Department of Orthodontics, Asan Medical Centre, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul 05505 Republic of Korea
| | - Taeyong Kim
- Department of Advanced General Dentistry, Kangnam Sacred Heart Hospital, Hallym University, Singil-ro 1-gil, Yeongdeungpo-gu, Seoul 07441, Republic of Korea
| | - Jonghun Yoon
- Department of Mechanical Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan-si, Gyeonggi-do 15588, Republic of Korea; BK21 FOUR ERICA-ACE Centre, Hanyang University, Ansan-si, Gyeonggi-do 15588, Republic of Korea.
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Spinopelvic measurements of sagittal balance with deep learning: systematic review and critical evaluation. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2022; 31:2031-2045. [PMID: 35278146 DOI: 10.1007/s00586-022-07155-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 02/04/2022] [Accepted: 02/14/2022] [Indexed: 01/20/2023]
Abstract
PURPOSE To summarize and critically evaluate the existing studies for spinopelvic measurements of sagittal balance that are based on deep learning (DL). METHODS Three databases (PubMed, WoS and Scopus) were queried for records using keywords related to DL and measurement of sagittal balance. After screening the resulting 529 records that were augmented with specific web search, 34 studies published between 2017 and 2022 were included in the final review, and evaluated from the perspective of the observed sagittal spinopelvic parameters, properties of spine image datasets, applied DL methodology and resulting measurement performance. RESULTS Studies reported DL measurement of up to 18 different spinopelvic parameters, but the actual number depended on the image field of view. Image datasets were composed of lateral lumbar spine and whole spine X-rays, biplanar whole spine X-rays and lumbar spine magnetic resonance cross sections, and were increasing in size or enriched by augmentation techniques. Spinopelvic parameter measurement was approached either by landmark detection or structure segmentation, and U-Net was the most frequently applied DL architecture. The latest DL methods achieved excellent performance in terms of mean absolute error against reference manual measurements (~ 2° or ~ 1 mm). CONCLUSION Although the application of relatively complex DL architectures resulted in an improved measurement accuracy of sagittal spinopelvic parameters, future methods should focus on multi-institution and multi-observer analyses as well as uncertainty estimation and error handling implementations for integration into the clinical workflow. Further advances will enhance the predictive analytics of DL methods for spinopelvic parameter measurement. LEVEL OF EVIDENCE I Diagnostic: individual cross-sectional studies with the consistently applied reference standard and blinding.
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D’Antoni F, Russo F, Ambrosio L, Bacco L, Vollero L, Vadalà G, Merone M, Papalia R, Denaro V. Artificial Intelligence and Computer Aided Diagnosis in Chronic Low Back Pain: A Systematic Review. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19105971. [PMID: 35627508 PMCID: PMC9141006 DOI: 10.3390/ijerph19105971] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 05/09/2022] [Accepted: 05/12/2022] [Indexed: 12/10/2022]
Abstract
Low Back Pain (LBP) is currently the first cause of disability in the world, with a significant socioeconomic burden. Diagnosis and treatment of LBP often involve a multidisciplinary, individualized approach consisting of several outcome measures and imaging data along with emerging technologies. The increased amount of data generated in this process has led to the development of methods related to artificial intelligence (AI), and to computer-aided diagnosis (CAD) in particular, which aim to assist and improve the diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of CAD in the diagnosis and treatment of chronic LBP. A systematic research of PubMed, Scopus, and Web of Science electronic databases was performed. The search strategy was set as the combinations of the following keywords: “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Neural Network”, “Computer Aided Diagnosis”, “Low Back Pain”, “Lumbar”, “Intervertebral Disc Degeneration”, “Spine Surgery”, etc. The search returned a total of 1536 articles. After duplication removal and evaluation of the abstracts, 1386 were excluded, whereas 93 papers were excluded after full-text examination, taking the number of eligible articles to 57. The main applications of CAD in LBP included classification and regression. Classification is used to identify or categorize a disease, whereas regression is used to produce a numerical output as a quantitative evaluation of some measure. The best performing systems were developed to diagnose degenerative changes of the spine from imaging data, with average accuracy rates >80%. However, notable outcomes were also reported for CAD tools executing different tasks including analysis of clinical, biomechanical, electrophysiological, and functional imaging data. Further studies are needed to better define the role of CAD in LBP care.
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Affiliation(s)
- Federico D’Antoni
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
| | - Fabrizio Russo
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
- Correspondence: (F.R.); (M.M.)
| | - Luca Ambrosio
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Luca Bacco
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
- ItaliaNLP Lab, Istituto di Linguistica Computazionale “Antonio Zampolli”, National Research Council, Via Giuseppe Moruzzi, 1, 56124 Pisa, Italy
- Webmonks S.r.l., Via del Triopio, 5, 00178 Rome, Italy
| | - Luca Vollero
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
| | - Gianluca Vadalà
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Mario Merone
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 21, 00128 Rome, Italy; (F.D.); (L.B.); (L.V.)
- Correspondence: (F.R.); (M.M.)
| | - Rocco Papalia
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
| | - Vincenzo Denaro
- Department of Orthopaedic Surgery, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo, 200, 00128 Rome, Italy; (L.A.); (G.V.); (R.P.); (V.D.)
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Abstract
Recently, deep learning algorithms have become one of the most popular methods and forms of algorithms used in the medical imaging analysis process. Deep learning tools provide accuracy and speed in the process of diagnosing and classifying lumbar spine problems. Disk herniation and spinal stenosis are two of the most common lower back diseases. The process of diagnosing pain in the lower back can be considered costly in terms of time and available expertise. In this paper, we used multiple approaches to overcome the problem of lack of training data in disc state classification and to enhance the performance of disc state classification tasks. To achieve this goal, transfer learning from different datasets and a proposed region of interest (ROI) technique were implemented. It has been demonstrated that using transfer learning from the same domain as the target dataset may increase performance dramatically. Applying the ROI method improved the disc state classification results in VGG19 2%, ResNet50 16%, MobileNetV2 5%, and VGG16 2%. The results improved VGG16 4% and in VGG19 6%, compared with the transfer from ImageNet. Moreover, it has been stated that the closer the data to be classified is to the data that the system trained on, the better the achieved results will be.
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Patel AV, White CA, Schwartz JT, Pitaro NL, Shah KC, Singh S, Arvind V, Kim JS, Cho SK. Emerging Technologies in the Treatment of Adult Spinal Deformity. Neurospine 2021; 18:417-427. [PMID: 34610669 PMCID: PMC8497255 DOI: 10.14245/ns.2142412.206] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 07/12/2021] [Indexed: 12/29/2022] Open
Abstract
Outcomes for adult spinal deformity continue to improve as new technologies become integrated into clinical practice. Machine learning, robot-guided spinal surgery, and patient-specific rods are tools that are being used to improve preoperative planning and patient satisfaction. Machine learning can be used to predict complications, readmissions, and generate postoperative radiographs which can be shown to patients to guide discussions about surgery. Robot-guided spinal surgery is a rapidly growing field showing signs of greater accuracy in screw placement during surgery. Patient-specific rods offer improved outcomes through higher correction rates and decreased rates of rod breakage while decreasing operative time. The objective of this review is to evaluate trends in the literature about machine learning, robot-guided spinal surgery, and patient-specific rods in the treatment of adult spinal deformity.
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Affiliation(s)
- Akshar V Patel
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Christopher A White
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John T Schwartz
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicholas L Pitaro
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kush C Shah
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sirjanhar Singh
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Varun Arvind
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jun S Kim
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samuel K Cho
- Department of Orthopaedic Surgery, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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