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Flanders WH, Moïse NS, Otani NF. Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs. J Vet Intern Med 2024; 38:1305-1324. [PMID: 38682817 PMCID: PMC11099791 DOI: 10.1111/jvim.17071] [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: 10/12/2023] [Accepted: 03/27/2024] [Indexed: 05/01/2024] Open
Abstract
BACKGROUND Sinus node dysfunction because of abnormal impulse generation or sinoatrial conduction block causes bradycardia that can be difficult to differentiate from high parasympathetic/low sympathetic modulation (HP/LSM). HYPOTHESIS Beat-to-beat relationships of sinus node dysfunction are quantifiably distinguishable by Poincaré plots, machine learning, and 3-dimensional density grid analysis. Moreover, computer modeling establishes sinoatrial conduction block as a mechanism. ANIMALS Three groups of dogs were studied with a diagnosis of: (1) balanced autonomic modulation (n = 26), (2) HP/LSM (n = 26), and (3) sinus node dysfunction (n = 21). METHODS Heart rate parameters and Poincaré plot data were determined [median (25%-75%)]. Recordings were randomly assigned to training or testing. Supervised machine learning of the training data was evaluated with the testing data. The computer model included impulse rate, exit block probability, and HP/LSM. RESULTS Confusion matrices illustrated the effectiveness in diagnosing by both machine learning and Poincaré density grid. Sinus pauses >2 s differentiated (P < .0001) HP/LSM (2340; 583-3947 s) from sinus node dysfunction (8503; 7078-10 050 s), but average heart rate did not. The shortest linear intervals were longer with sinus node dysfunction (315; 278-323 ms) vs HP/LSM (260; 251-292 ms; P = .008), but the longest linear intervals were shorter with sinus node dysfunction (620; 565-698 ms) vs HP/LSM (843; 799-888 ms; P < .0001). CONCLUSIONS Number and duration of pauses, not heart rate, differentiated sinus node dysfunction from HP/LSM. Machine learning and Poincaré density grid can accurately identify sinus node dysfunction. Computer modeling supports sinoatrial conduction block as a mechanism of sinus node dysfunction.
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Affiliation(s)
- Wyatt Hutson Flanders
- Department of Clinical Sciences, College of Veterinary MedicineCornell UniversityIthacaNew YorkUSA
| | - N. Sydney Moïse
- Section of Cardiology, Department of Clinical Sciences, College of Veterinary MedicineCornell UniversityIthacaNew YorkUSA
| | - Niels F. Otani
- School of Mathematical SciencesRochester Institute of TechnologyRochesterNew YorkUSA
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Smith A, Carroll PW, Aravamuthan S, Walleser E, Lin H, Anklam K, Döpfer D, Apostolopoulos N. Computer vision model for the detection of canine pododermatitis and neoplasia of the paw. Vet Dermatol 2024; 35:138-147. [PMID: 38057947 DOI: 10.1111/vde.13221] [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: 04/03/2023] [Revised: 09/01/2023] [Accepted: 11/20/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has been used successfully in human dermatology. AI utilises convolutional neural networks (CNN) to accomplish tasks such as image classification, object detection and segmentation, facilitating early diagnosis. Computer vision (CV), a field of AI, has shown great results in detecting signs of human skin diseases. Canine paw skin diseases are a common problem in general veterinary practice, and computer vision tools could facilitate the detection and monitoring of disease processes. Currently, no such tool is available in veterinary dermatology. ANIMALS Digital images of paws from healthy dogs and paws with pododermatitis or neoplasia were used. OBJECTIVES We tested the novel object detection model Pawgnosis, a Tiny YOLOv4 image analysis model deployed on a microcomputer with a camera for the rapid detection of canine pododermatitis and neoplasia. MATERIALS AND METHODS The prediction performance metrics used to evaluate the models included mean average precision (mAP), precision, recall, average precision (AP) for accuracy and frames per second (FPS) for speed. RESULTS A large dataset labelled by a single individual (Dataset A) used to train a Tiny YOLOv4 model provided the best results with a mean mAP of 0.95, precision of 0.86, recall of 0.93 and 20 FPS. CONCLUSIONS AND CLINICAL RELEVANCE This novel object detection model has the potential for application in the field of veterinary dermatology.
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Affiliation(s)
- Andrew Smith
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Patrick W Carroll
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Srikanth Aravamuthan
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Emil Walleser
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Haley Lin
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Kelly Anklam
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Dörte Döpfer
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
| | - Neoklis Apostolopoulos
- School of Veterinary Medicine, Department of Medical Sciences, University of Wisconsin in Madison, Madison, Wisconsin, USA
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Nyquist ML, Fink LA, Mauldin GE, Coffman CR. Evaluation of a Novel Veterinary Dental Radiography Artificial Intelligence Software Program. J Vet Dent 2024:8987564231221071. [PMID: 38321886 DOI: 10.1177/08987564231221071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
There is a growing trend of artificial intelligence (AI) applications in veterinary medicine, with the potential to assist veterinarians in clinical decisions. A commercially available, AI-based software program (AISP) for detecting common radiographic dental pathologies in dogs and cats was assessed for agreement with two human evaluators. Furcation bone loss, periapical lucency, resorptive lesion, retained tooth root, attachment (alveolar bone) loss and tooth fracture were assessed. The AISP does not attempt to diagnose or provide treatment recommendations, nor has it been trained to identify other types of radiographic pathology. Inter-rater reliability for detecting pathologies was measured by absolute percent agreement and Gwet's agreement coefficient. There was good to excellent inter-rater reliability among all raters, suggesting the AISP performs similarly at detecting the specified pathologies compared to human evaluators. Sensitivity and specificity for the AISP were assessed using human evaluators as the reference standard. The results revealed a trend of low sensitivity and high specificity, suggesting the AISP may produce a high rate of false negatives and may not be a good tool for initial screening. However, the low rate of false positives produced by the AISP suggests it may be beneficial as a "second set of eyes" because if it detects the specific pathology, there is a high likelihood that the pathology is present. With an understanding of the AISP, as an aid and not a substitute for veterinarians, the technology may increase dental radiography utilization and diagnostic potential.
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Affiliation(s)
| | - Lisa A Fink
- Arizona Veterinary Dental Specialists, Scottsdale, AZ, USA
| | | | - Curt R Coffman
- Arizona Veterinary Dental Specialists, Scottsdale, AZ, USA
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Hattab J, Porrello A, Romano A, Rosamilia A, Ghidini S, Bernabò N, Capobianco Dondona A, Corradi A, Marruchella G. Scoring Enzootic Pneumonia-like Lesions in Slaughtered Pigs: Traditional vs. Artificial-Intelligence-Based Methods. Pathogens 2023; 12:1460. [PMID: 38133343 PMCID: PMC10747234 DOI: 10.3390/pathogens12121460] [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: 11/06/2023] [Revised: 12/08/2023] [Accepted: 12/14/2023] [Indexed: 12/23/2023] Open
Abstract
Artificial-intelligence-based methods are regularly used in the biomedical sciences, mainly in the field of diagnostic imaging. Recently, convolutional neural networks have been trained to score pleurisy and pneumonia in slaughtered pigs. The aim of this study is to further evaluate the performance of a convolutional neural network when compared with the gold standard (i.e., scores provided by a skilled operator along the slaughter chain through visual inspection and palpation). In total, 441 lungs (180 healthy and 261 diseased) are included in this study. Each lung was scored according to traditional methods, which represent the gold standard (Madec's and Christensen's grids). Moreover, the same lungs were photographed and thereafter scored by a trained convolutional neural network. Overall, the results reveal that the convolutional neural network is very specific (95.55%) and quite sensitive (85.05%), showing a rather high correlation when compared with the scores provided by a skilled veterinarian (Spearman's coefficient = 0.831, p < 0.01). In summary, this study suggests that convolutional neural networks could be effectively used at slaughterhouses and stimulates further investigation in this field of research.
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Affiliation(s)
- Jasmine Hattab
- Department of Veterinary Medicine, University of Teramo, Loc. Piano d’Accio, 64100 Teramo, Italy;
| | - Angelo Porrello
- AImageLab, University of Modena and Reggio Emilia, Via Vivarelli 10/1, 41125 Modena, Italy;
| | - Anastasia Romano
- Associació Porcsa. GSP, Partida La Caparrella 97C, 25192 Lleida, Spain;
| | - Alfonso Rosamilia
- Istituto Zooprofilattico Sperimentale della Lombardia e dell’Emilia-Romagna “Bruno Ubertini” (IZSLER), 25124 Brescia, Italy;
| | - Sergio Ghidini
- Department of Food and Drug, University of Parma, Via del Taglio 10, 43126 Parma, Italy;
| | - Nicola Bernabò
- Department of Bioscience and Technology for Food, Agriculture and Environment, University of Teramo, Via Renato Balzarini 1, 64100 Teramo, Italy;
| | | | - Attilio Corradi
- Department of Veterinary Science, University of Parma, Via del Taglio 10, 43126 Parma, Italy;
| | - Giuseppe Marruchella
- Department of Veterinary Medicine, University of Teramo, Loc. Piano d’Accio, 64100 Teramo, Italy;
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Dank G, Buber T, Rice A, Kraicer N, Hanael E, Shasha T, Aviram G, Yehudayoff A, Kent MS. Training and validation of a novel non-invasive imaging system for ruling out malignancy in canine subcutaneous and cutaneous masses using machine learning in 664 masses. Front Vet Sci 2023; 10:1164438. [PMID: 37841459 PMCID: PMC10570610 DOI: 10.3389/fvets.2023.1164438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Accepted: 09/05/2023] [Indexed: 10/17/2023] Open
Abstract
Objective To train and validate the use of a novel artificial intelligence-based thermal imaging system as a screening tool to rule out malignancy in cutaneous and subcutaneous masses in dogs. Animals Training study: 147 client-owned dogs with 233 masses. Validation Study: 299 client-owned dogs with 525 masses. Cytology was non-diagnostic in 94 masses, resulting in 431 masses from 248 dogs with diagnostic samples. Procedures The prospective studies were conducted between June 2020 and July 2022. During the scan, each mass and its adjacent healthy tissue was heated by a high-power Light-Emitting Diode. The tissue temperature was recorded by the device and consequently analyzed using a supervised machine learning algorithm to determine whether the mass required further investigation. The first study was performed to collect data to train the algorithm. The second study validated the algorithm, as the real-time device predictions were compared to the cytology and/or biopsy results. Results The results for the validation study were that the device correctly classified 45 out of 53 malignant masses and 253 out of 378 benign masses (sensitivity = 85% and specificity = 67%). The negative predictive value of the system (i.e., percent of benign masses identified as benign) was 97%. Clinical relevance The results demonstrate that this novel system could be used as a decision-support tool at the point of care, enabling clinicians to differentiate between benign lesions and those requiring further diagnostics.
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Affiliation(s)
| | - Tali Buber
- HT BioImaging Ltd., Hod Hasharon, Israel
| | - Anna Rice
- HT BioImaging Ltd., Hod Hasharon, Israel
| | | | | | | | - Gal Aviram
- Department Biomedical Engineering, Tel Aviv University, Tel Aviv-Yafo, Israel
| | | | - Michael S. Kent
- Department of Surgical and Radiological Sciences, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
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Steffey MA, Griffon DJ, Risselada M, Scharf VF, Buote NJ, Zamprogno H, Winter AL. Veterinarian burnout demographics and organizational impacts: a narrative review. Front Vet Sci 2023; 10:1184526. [PMID: 37470072 PMCID: PMC10352684 DOI: 10.3389/fvets.2023.1184526] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Accepted: 06/19/2023] [Indexed: 07/21/2023] Open
Abstract
Burnout is a work-related syndrome of physical and emotional exhaustion secondary to prolonged, unresolvable occupational stress. Individuals of different demographic cohorts may have disparate experiences of workplace stressors and burnout impacts. Healthcare organizations are adversely affected by burnt out workers through decreased productivity, low morale, suboptimal teamwork, and potential impacts on the quality of patient care. In this second of two companion reviews, the demographics of veterinary burnout and the impacts of burnout on affected individuals and work environments are summarized, before discussing mitigation concepts and their extrapolation for targeted strategies within the veterinary workplace and profession.
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Affiliation(s)
- Michele A. Steffey
- Department of Surgical and Radiological Sciences, School of Veterinary Medicine, University of California, Davis, Davis, CA, United States
| | - Dominique J. Griffon
- Western University of Health Sciences, College of Veterinary Medicine, Pomona, CA, United States
| | - Marije Risselada
- Department of Veterinary Clinical Sciences, College of Veterinary Medicine, Purdue University, West-Lafayette, IN, United States
| | - Valery F. Scharf
- Department of Clinical Sciences, North Carolina State University College of Veterinary Medicine, Raleigh, NC, United States
| | - Nicole J. Buote
- Department of Clinical Sciences, Cornell University College of Veterinary Medicine, Ithaca, NY, United States
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Dewsbury DMA, Renter DG, Bradford BJ, DeDonder KD, Mellencamp M, Cernicchiaro N. The application, value, and impact of outcomes research in animal health and veterinary medicine. Front Vet Sci 2022; 9:972057. [DOI: 10.3389/fvets.2022.972057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 11/14/2022] [Indexed: 11/30/2022] Open
Abstract
Outcomes research is a relatively recent field of study in animal health and veterinary medicine despite being well-established in human medicine. As the field of animal health is broad-ranging in terms of animal species, objectives, research methodologies, design, analysis, values, and outcomes, there is inherent versatility in the application and impact of the discipline of outcomes research to a variety of stakeholders. The major themes of outcomes relevant to the animal health industry have been distilled down to include, but are not limited to, health, production, economics, and marketing. An outcomes research approach considers an element of value along with an outcome of interest, setting it apart from traditional research approaches. Elements of value are determined by the stakeholders' use of products and/or services that meet or exceed functional, emotional, life-changing, and/or societal needs. Stakeholder perception of value depends on many factors such as the purpose of the animal (e.g., companion vs. food production) and the stakeholder's role (e.g., veterinarian, client, pet-owner, producer, consumer, government official, industry representative, policy holder). Key areas of application of outcomes research principles include comparative medicine, veterinary product development, and post-licensure evaluation of veterinary pharmaceuticals and/or biologics. Topics currently trending in human healthcare outcomes research, such as drug pricing, precision medicine, or the use of real-world evidence, offer novel and interesting perspectives for addressing themes common to the animal health sector. An approach that evaluates the benefits of practices and interventions to veterinary patients and society while maximizing outcomes is paramount to combating many current and future scientific challenges where feeding the world, caring for our aging companion animals, and implementing novel technologies in companion animal medicine and in production animal agriculture are at the forefront of our industry goals.
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Radiomics Modeling of Catastrophic Proximal Sesamoid Bone Fractures in Thoroughbred Racehorses Using μCT. Animals (Basel) 2022; 12:ani12213033. [DOI: 10.3390/ani12213033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022] Open
Abstract
Proximal sesamoid bone (PSB) fractures are the most common musculoskeletal injury in race-horses. X-ray CT imaging can detect expressed radiological features in horses that experienced catastrophic fractures. Our objective was to assess whether expressed radiomic features in the PSBs of 50 horses can be used to develop machine learning models for predicting PSB fractures. The μCTs of intact contralateral PSBs from 50 horses, 30 of which suffered catastrophic fractures, and 20 controls were studied. From the 129 intact μCT images of PSBs, 102 radiomic features were computed using a variety of voxel resampling dimensions. Decision Trees and Wrapper methods were used to identify the 20 top expressed features, and six machine learning algorithms were developed to model the risk of fracture. The accuracy of all machine learning models ranged from 0.643 to 0.903 with an average of 0.754. On average, Support Vector Machine, Random Forest (RUS Boost), and Log-regression models had higher performance than K-means Nearest Neighbor, Neural Network, and Random Forest (Bagged Trees) models. Model accuracy peaked at 0.5 mm and decreased substantially when the resampling resolution was greater than or equal to 1 mm. We find that, for this in vitro dataset, it is possible to differentiate between unfractured PSBs from case and control horses using μCT images. It may be possible to extend these findings to the assessment of fracture risk in standing horses.
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