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Del Giorno S, D’Antoni F, Piemonte V, Merone M. A New Glycemic closed-loop control based on Dyna-Q for Type-1-Diabetes. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Bacco L, Russo F, Ambrosio L, D’Antoni F, Vollero L, Vadalà G, Dell’Orletta F, Merone M, Papalia R, Denaro V. Natural language processing in low back pain and spine diseases: A systematic review. Front Surg 2022; 9:957085. [PMID: 35910476 PMCID: PMC9329654 DOI: 10.3389/fsurg.2022.957085] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Accepted: 06/27/2022] [Indexed: 11/13/2022] Open
Abstract
Natural Language Processing (NLP) is a discipline at the intersection between Computer Science (CS), Artificial Intelligence (AI), and Linguistics that leverages unstructured human-interpretable (natural) language text. In recent years, it gained momentum also in health-related applications and research. Although preliminary, studies concerning Low Back Pain (LBP) and other related spine disorders with relevant applications of NLP methodologies have been reported in the literature over the last few years. It motivated us to systematically review the literature comprised of two major public databases, PubMed and Scopus. To do so, we first formulated our research question following the PICO guidelines. Then, we followed a PRISMA-like protocol by performing a search query including terminologies of both technical (e.g., natural language and computational linguistics) and clinical (e.g., lumbar and spine surgery) domains. We collected 221 non-duplicated studies, 16 of which were eligible for our analysis. In this work, we present these studies divided into sub-categories, from both tasks and exploited models’ points of view. Furthermore, we report a detailed description of techniques used to extract and process textual features and the several evaluation metrics used to assess the performance of the NLP models. However, what is clear from our analysis is that additional studies on larger datasets are needed to better define the role of NLP in the care of patients with spinal disorders.
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Affiliation(s)
- Luca Bacco
- Department of Engineering, Unit of Computer Systems and Bioinformatics, Campus Bio-Medico University of Rome, Rome, Italy
- ItaliaNLP Lab, National Research Council, Istituto di Linguistica Computazionale “Antonio Zampolli”, Pisa, Italy
- R&D Lab, Webmonks S.r.l., Rome, Italy
| | - Fabrizio Russo
- Department of Orthopaedic and Trauma Surgery, Campus Bio-Medico University Hospital Foundation, Rome, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Campus Bio-Medico University of Rome, Rome, Italy
- Correspondence: Mario Merone Fabrizio Russo
| | - Luca Ambrosio
- Research Unit of Orthopaedic and Trauma Surgery, Campus Bio-Medico University of Rome, Rome, Italy
| | - Federico D’Antoni
- Department of Engineering, Unit of Computer Systems and Bioinformatics, Campus Bio-Medico University of Rome, Rome, Italy
| | - Luca Vollero
- Department of Engineering, Unit of Computer Systems and Bioinformatics, Campus Bio-Medico University of Rome, Rome, Italy
| | - Gianluca Vadalà
- Department of Orthopaedic and Trauma Surgery, Campus Bio-Medico University Hospital Foundation, Rome, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Campus Bio-Medico University of Rome, Rome, Italy
| | - Felice Dell’Orletta
- ItaliaNLP Lab, National Research Council, Istituto di Linguistica Computazionale “Antonio Zampolli”, Pisa, Italy
| | - Mario Merone
- Department of Engineering, Unit of Computer Systems and Bioinformatics, Campus Bio-Medico University of Rome, Rome, Italy
- Correspondence: Mario Merone Fabrizio Russo
| | - Rocco Papalia
- Department of Orthopaedic and Trauma Surgery, Campus Bio-Medico University Hospital Foundation, Rome, Italy
- Research Unit of Orthopaedic and Trauma Surgery, Campus Bio-Medico University of Rome, Rome, Italy
| | - Vincenzo Denaro
- Department of Orthopaedic and Trauma Surgery, Campus Bio-Medico University Hospital Foundation, Rome, Italy
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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. Int J Environ Res Public Health 2022; 19:ijerph19105971. [PMID: 35627508 PMCID: PMC9141006 DOI: 10.3390/ijerph19105971] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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D’Antoni F, Petrosino L, Sgarro F, Pagano A, Vollero L, Piemonte V, Merone M. Prediction of Glucose Concentration in Children with Type 1 Diabetes Using Neural Networks: An Edge Computing Application. Bioengineering (Basel) 2022; 9:bioengineering9050183. [PMID: 35621461 PMCID: PMC9137786 DOI: 10.3390/bioengineering9050183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/14/2022] [Accepted: 04/18/2022] [Indexed: 11/16/2022] Open
Abstract
Background: Type 1 Diabetes Mellitus (T1D) is an autoimmune disease that can cause serious complications that can be avoided by preventing the glycemic levels from exceeding the physiological range. Straightforwardly, many data-driven models were developed to forecast future glycemic levels and to allow patients to avoid adverse events. Most models are tuned on data of adult patients, whereas the prediction of glycemic levels of pediatric patients has been rarely investigated, as they represent the most challenging T1D population. Methods: A Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) Recurrent Neural Network were optimized on glucose, insulin, and meal data of 10 virtual pediatric patients. The trained models were then implemented on two edge-computing boards to evaluate the feasibility of an edge system for glucose forecasting in terms of prediction accuracy and inference time. Results: The LSTM model achieved the best numeric and clinical accuracy when tested in the .tflite format, whereas the CNN achieved the best clinical accuracy in uint8. The inference time for each prediction was far under the limit represented by the sampling period. Conclusion: Both models effectively predict glucose in pediatric patients in terms of numerical and clinical accuracy. The edge implementation did not show a significant performance decrease, and the inference time was largely adequate for a real-time application.
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Affiliation(s)
- Federico D’Antoni
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
- Correspondence: (F.D.); (M.M.)
| | - Lorenzo Petrosino
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
| | - Fabiola Sgarro
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
| | - Antonio Pagano
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
| | - Luca Vollero
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
| | - Vincenzo Piemonte
- Unit of Chemical Engineering, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Mario Merone
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (L.P.); (F.S.); (A.P.); (L.V.)
- Correspondence: (F.D.); (M.M.)
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D’Antoni F, Russo F, Ambrosio L, Vollero L, Vadalà G, Merone M, Papalia R, Denaro V. Artificial Intelligence and Computer Vision in Low Back Pain: A Systematic Review. Int J Environ Res Public Health 2021; 18:ijerph182010909. [PMID: 34682647 PMCID: PMC8535895 DOI: 10.3390/ijerph182010909] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 10/04/2021] [Accepted: 10/09/2021] [Indexed: 12/16/2022]
Abstract
Chronic Low Back Pain (LBP) is a symptom that may be caused by several diseases, and it is currently the leading cause of disability worldwide. The increased amount of digital images in orthopaedics has led to the development of methods related to artificial intelligence, and to computer vision in particular, which aim to improve diagnosis and treatment of LBP. In this manuscript, we have systematically reviewed the available literature on the use of computer vision in the diagnosis and treatment of LBP. A systematic research of PubMed electronic database was performed. The search strategy was set as the combinations of the following keywords: "Artificial Intelligence", "Feature Extraction", "Segmentation", "Computer Vision", "Machine Learning", "Deep Learning", "Neural Network", "Low Back Pain", "Lumbar". Results: The search returned a total of 558 articles. After careful evaluation of the abstracts, 358 were excluded, whereas 124 papers were excluded after full-text examination, taking the number of eligible articles to 76. The main applications of computer vision in LBP include feature extraction and segmentation, which are usually followed by further tasks. Most recent methods use deep learning models rather than digital image processing techniques. The best performing methods for segmentation of vertebrae, intervertebral discs, spinal canal and lumbar muscles achieve Sørensen-Dice scores greater than 90%, whereas studies focusing on localization and identification of structures collectively showed an accuracy greater than 80%. Future advances in artificial intelligence are expected to increase systems' autonomy and reliability, thus providing even more effective tools for the diagnosis and treatment of LBP.
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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.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 Vollero
- Unit of Computer Systems and Bioinformatics, Università Campus Bio-Medico di Roma, Via Alvaro Del Portillo 21, 00128 Rome, Italy; (F.D.); (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.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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De Paoli B, D’Antoni F, Merone M, Pieralice S, Piemonte V, Pozzilli P. Blood Glucose Level Forecasting on Type-1-Diabetes Subjects during Physical Activity: A Comparative Analysis of Different Learning Techniques. Bioengineering (Basel) 2021; 8:bioengineering8060072. [PMID: 34073433 PMCID: PMC8229703 DOI: 10.3390/bioengineering8060072] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/17/2021] [Accepted: 05/22/2021] [Indexed: 01/26/2023] Open
Abstract
Background: Type 1 Diabetes Mellitus (T1DM) is a widespread chronic disease in industrialized countries. Preventing blood glucose levels from exceeding the euglycaemic range would reduce the incidence of diabetes-related complications and improve the quality of life of subjects with T1DM. As a consequence, in the last decade, many Machine Learning algorithms aiming to forecast future blood glucose levels have been proposed. Despite the excellent performance they obtained, the prediction of abrupt changes in blood glucose values produced during physical activity (PA) is still one of the main challenges. Methods: A Jump Neural Network was developed in order to overcome the issue of predicting blood glucose values during PA. Three learning configurations were developed and tested: offline training, online training, and online training with reinforcement. All configurations were tested on six subjects suffering from T1DM that held regular PA (three aerobic and three anaerobic) and exploited Continuous Glucose Monitoring (CGM). Results: The forecasting performance was evaluated in terms of the Root-Mean-Squared-Error (RMSE), according to a paradigm of Precision Medicine. Conclusions: The online learning configurations performed better than the offline configuration in total days but not on the only CGM associated with the PA; thus, the results do not justify the increased computational burden because the improvement was not significant.
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Affiliation(s)
- Benedetta De Paoli
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (B.D.P.); (F.D.)
| | - Federico D’Antoni
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (B.D.P.); (F.D.)
| | - Mario Merone
- Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (B.D.P.); (F.D.)
- Correspondence: ; Tel.: +39-06-225-419-622
| | - Silvia Pieralice
- Unit of Diabetology and Endocrinology, Department of Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (S.P.); (P.P.)
| | - Vincenzo Piemonte
- Unit of Chemical Engineering, Department of Engineering, Università Campus Bio-Medico di Roma, 00128 Rome, Italy;
| | - Paolo Pozzilli
- Unit of Diabetology and Endocrinology, Department of Medicine, Università Campus Bio-Medico di Roma, 00128 Rome, Italy; (S.P.); (P.P.)
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D’Antoni F, Merone M, Piemonte V, Iannello G, Soda P. Auto-Regressive Time Delayed jump neural network for blood glucose levels forecasting. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2020.106134] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
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