1
|
Tumko V, Kim J, Uspenskaia N, Honig S, Abel F, Lebl DR, Hotalen I, Kolisnyk S, Kochnev M, Rusakov A, Mourad R. A neural network model for detection and classification of lumbar spinal stenosis on MRI. Eur Spine J 2024; 33:941-948. [PMID: 38150003 DOI: 10.1007/s00586-023-08089-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 10/30/2023] [Accepted: 12/04/2023] [Indexed: 12/28/2023]
Abstract
OBJECTIVES To develop a three-stage convolutional neural network (CNN) approach to segment anatomical structures, classify the presence of lumbar spinal stenosis (LSS) for all 3 stenosis types: central, lateral recess and foraminal and assess its severity on spine MRI and to demonstrate its efficacy as an accurate and consistent diagnostic tool. METHODS The three-stage model was trained on 1635 annotated lumbar spine MRI studies consisting of T2-weighted sagittal and axial planes at each vertebral level. Accuracy of the model was evaluated on an external validation set of 150 MRI studies graded on a scale of absent, mild, moderate or severe by a panel of 7 radiologists. The reference standard for all types was determined by majority voting and in case of disagreement, adjudicated by an external radiologist. The radiologists' diagnoses were then compared to the diagnoses of the model. RESULTS The model showed comparable performance to the radiologist average both in terms of the determination of presence/absence of LSS as well as severity classification, for all 3 stenosis types. In the case of central canal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.971, 0.864, 0.963) for binary (presence/absence) classification compared to the radiologist average of (0.786, 0.899, 0.842). For lateral recess stenosis, the sensitivity, specificity and AUROC of the CNN were (0.853, 0.787, 0.907) compared to the radiologist average of (0.713, 0.898, 805). For foraminal stenosis, the sensitivity, specificity and AUROC of the CNN were (0.942, 0.844, 0.950) compared to the radiologist average of (0.879, 0.877, 0.878). Multi-class severity classifications showed similarly comparable statistics. CONCLUSIONS The CNN showed comparable performance to radiologist subspecialists for the detection and classification of LSS. The integration of neural network models in the detection of LSS could bring higher accuracy, efficiency, consistency, and post-hoc interpretability in diagnostic practices.
Collapse
Affiliation(s)
- Vladislav Tumko
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | - Jack Kim
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA.
| | - Natalia Uspenskaia
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | - Shaun Honig
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | - Frederik Abel
- Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Darren R Lebl
- Hospital for Special Surgery, 535 East 70th Street, New York, NY, 10021, USA
| | - Irene Hotalen
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | | | - Mikhail Kochnev
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | - Andrej Rusakov
- Remedy Logic, 1177 Avenue of the Americas, 5th Floor, New York, NY, 10036, USA
| | - Raphaël Mourad
- University of Toulouse, 118 Rte de Narbonne, 31062, Toulouse, France.
| |
Collapse
|
2
|
Abel F, Garcia E, Andreeva V, Nikolaev NS, Kolisnyk S, Sarbaev R, Novikov I, Kozinchenko E, Kim J, Rusakov A, Mourad R, Lebl DR. An Artificial Intelligence-Based Support Tool for Lumbar Spinal Stenosis Diagnosis from Self-Reported History Questionnaire. World Neurosurg 2024; 181:e953-e962. [PMID: 37952887 DOI: 10.1016/j.wneu.2023.11.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 11/04/2023] [Accepted: 11/06/2023] [Indexed: 11/14/2023]
Abstract
OBJECTIVES Symptomatic lumbar spinal stenosis (LSS) leads to functional impairment and pain. While radiologic characterization of the morphological stenosis grade can aid in the diagnosis, it may not always correlate with patient symptoms. Artificial intelligence (AI) may diagnose symptomatic LSS in patients solely based on self-reported history questionnaires. METHODS We evaluated multiple machine learning (ML) models to determine the likelihood of LSS using a self-reported questionnaire in patients experiencing low back pain and/or numbness in the legs. The questionnaire was built from peer-reviewed literature and a multidisciplinary panel of experts. Random forest, lasso logistic regression, support vector machine, gradient boosting trees, deep neural networks, and automated machine learning models were trained and performance metrics were compared. RESULTS Data from 4827 patients (4690 patients without LSS: mean age 62.44, range 27-84 years, 62.8% females, and 137 patients with LSS: mean age 50.59, range 30-71 years, 59.9% females) were retrospectively collected. Among the evaluated models, the random forest model demonstrated the highest predictive accuracy with an area under the receiver operating characteristic curve (AUROC) between model prediction and LSS diagnosis of 0.96, a sensitivity of 0.94, a specificity of 0.88, a balanced accuracy of 0.91, and a Cohen's kappa of 0.85. CONCLUSIONS Our results indicate that ML can automate the diagnosis of LSS based on self-reported questionnaires with high accuracy. Implementation of standardized and intelligence-automated workflow may serve as a supportive diagnostic tool to streamline patient management and potentially lower health care costs.
Collapse
Affiliation(s)
- Frederik Abel
- Department of Spine Surgery, Hospital for Special Surgery, New York, New York, USA
| | | | - Vera Andreeva
- Federal State Budgetary Institution, Federal Center for Traumatology, Orthopedics and Arthroplasty, Ministry of Health of the Russian Federation, Cheboksary, Russia
| | - Nikolai S Nikolaev
- Federal State Budgetary Institution, Federal Center for Traumatology, Orthopedics and Arthroplasty, Ministry of Health of the Russian Federation, Cheboksary, Russia; Federal State Budgetary Educational Institution of Higher Education, Chuvash State University named after I.N. Ulyanov, Cheboksary, Russia
| | - Serhii Kolisnyk
- Department of Physical and Rehabilitation Medicine, Vinnitsa National Medical University, Vinnytsia, Ukraine
| | | | | | | | - Jack Kim
- Remedy Logic, New York, New York, USA
| | | | - Raphael Mourad
- University of Toulouse, CNRS, UPS, Toulouse, France; Remedy Logic, New York, New York, USA.
| | - Darren R Lebl
- Department of Spine Surgery, Hospital for Special Surgery, New York, New York, USA
| |
Collapse
|
3
|
De Barros A, Abel F, Kolisnyk S, Geraci GC, Hill F, Engrav M, Samavedi S, Suldina O, Kim J, Rusakov A, Lebl DR, Mourad R. Determining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning. Global Spine J 2023:21925682231155844. [PMID: 36752058 DOI: 10.1177/21925682231155844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023] Open
Abstract
STUDY DESIGN Medical vignettes. OBJECTIVES Lumbar spinal stenosis (LSS) is a degenerative condition with a high prevalence in the elderly population, that is associated with a significant economic burden and often requires spinal surgery. Prior authorization of surgical candidates is required before patients can be covered by a health plan and must be approved by medical directors (MDs), which is often subjective and clinician specific. In this study, we hypothesized that the prediction accuracy of machine learning (ML) methods regarding surgical candidates is comparable to that of a panel of MDs. METHODS Based on patient demographic factors, previous therapeutic history, symptoms and physical examinations and imaging findings, we propose an ML which computes the probability of spinal surgical recommendations for LSS. The model implements a random forest model trained from medical vignette data reviewed by MDs. Sets of 400 and 100 medical vignettes reviewed by MDs were used for training and testing. RESULTS The predictive accuracy of the machine learning model was with a root mean square error (RMSE) between model predictions and ground truth of .1123, while the average RMSE between individual MD's recommendations and ground truth was .2661. For binary classification, the AUROC and Cohen's kappa were .959 and .801, while the corresponding average metrics based on individual MD's recommendations were .844 and .564, respectively. CONCLUSIONS Our results suggest that ML can be used to automate prior authorization approval of surgery for LSS with performance comparable to a panel of MDs.
Collapse
Affiliation(s)
- Amaury De Barros
- Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier-INSERM, Toulouse, France
- Neuroscience (Neurosurgery) Center, Toulouse University Hospital, Toulouse, France
| | | | | | | | | | | | | | | | | | | | | | - Raphael Mourad
- Remedy Logic, New York, NY, USA
- University of Toulouse, Toulouse, France
| |
Collapse
|
4
|
Pliss E, Machtin V, Soloviev M, Grobov A, Pliss R, Sirik A, Rusakov A. The Role of Solvation in the Kinetics and the Mechanism of Hydroperoxide Radicals Addition to π-Bonds of 1,2-Diphenylethylene and 1,4-Diphenylbutadiene-1,3. INT J CHEM KINET 2018. [DOI: 10.1002/kin.21169] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Affiliation(s)
- E. Pliss
- P. G. Demidov Yaroslavl' State University; 150003 Sovetskaya 14 Yaroslavl Russian Federation
| | - V. Machtin
- P. G. Demidov Yaroslavl' State University; 150003 Sovetskaya 14 Yaroslavl Russian Federation
| | - M. Soloviev
- P. G. Demidov Yaroslavl' State University; 150003 Sovetskaya 14 Yaroslavl Russian Federation
| | - A. Grobov
- P. G. Demidov Yaroslavl' State University; 150003 Sovetskaya 14 Yaroslavl Russian Federation
| | - R. Pliss
- P. G. Demidov Yaroslavl' State University; 150003 Sovetskaya 14 Yaroslavl Russian Federation
| | - A. Sirik
- P. G. Demidov Yaroslavl' State University; 150003 Sovetskaya 14 Yaroslavl Russian Federation
| | - A. Rusakov
- P. G. Demidov Yaroslavl' State University; 150003 Sovetskaya 14 Yaroslavl Russian Federation
| |
Collapse
|
5
|
Lednev S, Sirick A, Pliss E, Rusakov A, Shvyrkova N, Ivanov A. Influence of solvation on the kinetics of methyl methacrylate oxidation inhibited by aromatic amines. Reac Kinet Mech Cat 2015. [DOI: 10.1007/s11144-015-0881-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
6
|
Rusakov A, Medvinsky AB, Panfilov AV. Scroll waves meandering in a model of an excitable medium. Phys Rev E Stat Nonlin Soft Matter Phys 2005; 72:022902. [PMID: 16196618 DOI: 10.1103/physreve.72.022902] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2005] [Revised: 06/14/2005] [Indexed: 05/04/2023]
Abstract
We study numerically the dynamics of a scroll wave in a three-dimensional (3D) excitable medium in the presence of substantial meandering of the corresponding 2D spiral wave in the Aliev-Panfilov model. We identify three types of dynamics of the scroll wave filament--quasi-2D, periodic, and aperiodic meandering--and we study their dependence on parameter settings and thickness of the medium.
Collapse
Affiliation(s)
- A Rusakov
- Institute for Theoretical & Experimental Biophysics, Russian Academy of Sciences Pushchino, Moscow Region 142290, Russia
| | | | | |
Collapse
|
7
|
|