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Shahraki K, Suh DW. An Update to Biomechanical and Biochemical Principles of Retinal Injury in Child Abuse. CHILDREN (BASEL, SWITZERLAND) 2024; 11:586. [PMID: 38790581 PMCID: PMC11119297 DOI: 10.3390/children11050586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 05/10/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024]
Abstract
Abusive head trauma (AHT) is an extreme form of physical child abuse, a subset of which is shaken baby syndrome (SBS). While traumatic injury in children is most readily observed as marks of contusion on the body, AHT/SBS may result in internal injuries that can put the life of the child in danger. One pivotal sign associated with AHT/SBS that cannot be spotted with the naked eye is retinal injury (RI), an early sign of which is retinal hemorrhage (RH) in cases with rupture of the retinal vasculature. If not addressed, RI can lead to irreversible outcomes, such as visual loss. It is widely assumed that the major cause of RI is acceleration-deceleration forces that are repeatedly imposed on the patient during abusive shaking. Still, due to the controversial nature of this type of injury, few investigations have ever sought to delve into its biomechanical and/or biochemical features using realistic models. As such, our knowledge regarding AHT-/SBS-induced RI is significantly lacking. In this mini-review, we aim to provide an up-to-date account of the traumatology of AHT-/SBS-induced RI, as well as its biomechanical and biochemical features, while focusing on some of the experimental models that have been developed in recent years for studying retinal hemorrhage in the context of AHT/SBS.
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Affiliation(s)
| | - Donny W. Suh
- Department of Ophthalmology, Gavin Herbert Eye Institute, University of California Irvine School of Medicine, Irvine, CA 92697, USA;
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Hutchinson K, van Zandwijk JP, Vester MEM, Seth A, Bilo RAC, van Rijn RR, Loeve AJ. Modeling of inflicted head injury by shaking trauma in children: what can we learn? : Update to parts I&II: A systematic review of animal, mathematical and physical models. Forensic Sci Med Pathol 2024:10.1007/s12024-023-00765-5. [PMID: 38236351 DOI: 10.1007/s12024-023-00765-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/12/2023] [Indexed: 01/19/2024]
Abstract
Inflicted shaking trauma can cause injury in infants, but exact injury mechanisms remain unclear. Controversy exists, particularly in courts, whether additional causes such as impact are required to produce injuries found in cases of (suspected) shaking. Publication rates of studies on animal and biomechanical models of inflicted head injury by shaking trauma (IHI-ST) in infants continue rising. Dissention on the topic, combined with its legal relevance, makes maintaining an up-to-date, clear and accessible overview of the current knowledge-base on IHI-ST essential. The current work reviews recent (2017-2023) studies using models of IHI-ST, serving as an update to two previously published reviews. A systematic review was conducted in Scopus and PubMed for articles using animal, physical and mathematical models for IHI-ST. Using the PRISMA methodology, two researchers independently screened the publications. Two, five, and ten publications were included on animal, physical, and mathematical models of IHI-ST, respectively. Both animal model studies used rodents. It is unknown to what degree these can accurately represent IHI-ST. Physical models were used mostly to investigate gross head-kinematics during shaking. Most mathematical models were used to study local effects on the eye and the head's internal structures. All injury thresholds and material properties used were based on scaled adult or animal data. Shaking motions used as inputs for animal, physical and mathematical models were mostly greatly simplified. Future research should focus on using more accurate shaking inputs for models, and on developing or and validating accurate injury thresholds applicable for shaking.
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Affiliation(s)
- Kim Hutchinson
- Department of BioMechanical Engineering, Faculty of Mechanical, Maritime & Materials Engineering, Delft University of Technology, Mekelweg 2, 2628, Delft, CD, Netherlands
| | - Jan Peter van Zandwijk
- Division of Digital and Biometric Traces, Netherlands Forensic Institute, Laan Van Ypenburg 6, 2497, The Hague, GB, Netherlands
| | - Marloes E M Vester
- Care Needs Assessment Centre CIZ, Orteliuslaan 1000, 3500 GR, Utrecht, Netherlands
| | - Ajay Seth
- Department of BioMechanical Engineering, Faculty of Mechanical, Maritime & Materials Engineering, Delft University of Technology, Mekelweg 2, 2628, Delft, CD, Netherlands
| | - Rob A C Bilo
- Veilig Thuis Rotterdam Rijnmond (Center for the Reporting of Child Abuse, Domestic Violence and Elder Abuse), Paul Krugerstraat 181, 3072 GJ, Rotterdam, Netherlands
| | - Rick R van Rijn
- Department of Radiology and Nuclear Medicine, Academic Medical Center Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, Netherlands
- Department of Forensic Medicine, Netherlands Forensic Institute, Laan Van Ypenburg 6, 2497, The Hague, GB, Netherlands
| | - Arjo J Loeve
- Department of BioMechanical Engineering, Faculty of Mechanical, Maritime & Materials Engineering, Delft University of Technology, Mekelweg 2, 2628, Delft, CD, Netherlands.
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Khosravi P, Huck NA, Shahraki K, Hunter SC, Danza CN, Kim SY, Forbes BJ, Dai S, Levin AV, Binenbaum G, Chang PD, Suh DW. Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study. Int J Mol Sci 2023; 24:15105. [PMID: 37894785 PMCID: PMC10606803 DOI: 10.3390/ijms242015105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/29/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.
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Affiliation(s)
- Pooya Khosravi
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA;
| | - Nolan A. Huck
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - Kourosh Shahraki
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - Stephen C. Hunter
- School of Medicine, University of California, 900 University Ave, Riverside, CA 92521, USA;
| | - Clifford Neil Danza
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - So Young Kim
- Department of Ophthalmology, College of Medicine, Soonchunhyang University, Cheonan 31151, Chungcheongnam-do, Republic of Korea;
| | - Brian J. Forbes
- Division of Ophthalmology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (B.J.F.); (G.B.)
| | - Shuan Dai
- Department of Ophthalmology, Queensland Children’s Hospital, South Brisbane, QLD 4101, Australia;
| | - Alex V. Levin
- Department of Ophthalmology, Flaum Eye Institute, Golisano Children’s Hospital, Rochester, NY 14642, USA;
| | - Gil Binenbaum
- Division of Ophthalmology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (B.J.F.); (G.B.)
| | - Peter D. Chang
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA;
- Department of Radiological Sciences, School of Medicine, University of California, Irvine, CA 92697, USA
| | - Donny W. Suh
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
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Suh A, Lam M, Shokrollahi Y, Dong P, Gu L, Suh D. Quantifying the efficacy of protective eyewear in pediatric soccer-induced retinal injury. J AAPOS 2023; 27:131.e1-131.e6. [PMID: 37187408 DOI: 10.1016/j.jaapos.2023.02.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/09/2023] [Accepted: 02/20/2023] [Indexed: 05/17/2023]
Abstract
BACKGROUND Ocular injury is common in children playing sports. Sports-related eye injuries, if severe enough, can lead to permanent vision impairment. Soccer, the most popular sport in the world, is a sport in which players rarely use protective eyewear. The purpose of this study was to determine how eye injuries are induced by a soccer ball impact and to evaluate whether eye protection influences the effects of impact. METHODS A finite element (FE) computer simulation was used to simulate soccer ball trauma on a model of the eye with and without eye protection. Protective eyewear of different materials (polycarbonate and acrylic) was modeled to investigate the optimal medium for eye protection. Stress and strain experienced by the eyeball was quantified by the FE computer simulation in each model. RESULTS Protective eyewear was found to be effective in lowering ocular stress and strain by absorbing and redirecting energy from the ball. Compared to the unprotected eye model, polycarbonate eyewear reduced the average stress the retina experienced by 61%, whereas the acrylic model reduced the average stress by 40%. Polycarbonate and acrylic eyewear also reduced the maximum strain experienced by the retina by 69% and 47%, respectively, reducing the severity of deformations of the eye on impact. CONCLUSIONS These findings suggest that wearing protective eyewear, especially when made of polycarbonate, can be an effective means of reducing injury-inducing retinal stress. The use of eye protection is thus recommended for pediatric patients participating in soccer.
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Affiliation(s)
- Andrew Suh
- Tulane University, New Orleans, Louisiana
| | - Matthew Lam
- Creighton University School of Medicine, Omaha, Nebraska
| | - Yasin Shokrollahi
- Department of Biomedical and Mechanical Engineering, Florida Institute of Technology, Melbourne, Florida
| | - Pengfei Dong
- Department of Biomedical and Mechanical Engineering, Florida Institute of Technology, Melbourne, Florida
| | - Linxia Gu
- Department of Biomedical and Mechanical Engineering, Florida Institute of Technology, Melbourne, Florida
| | - Donny Suh
- Gavin Herbert Eye Institute, University of California Irvine, Department of Ophthalmology and Visual Sciences, Irvine, California.
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Shokrollahi Y, Dong P, Kaya M, Suh DW, Gu L. Rapid Prediction of Retina Stress and Strain Patterns in Soccer-Related Ocular Injury: Integrating Finite Element Analysis with Machine Learning Approach. Diagnostics (Basel) 2022; 12:1530. [PMID: 35885436 PMCID: PMC9319813 DOI: 10.3390/diagnostics12071530] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 06/15/2022] [Accepted: 06/20/2022] [Indexed: 11/16/2022] Open
Abstract
Soccer-related ocular injuries, especially retinal injuries, have attracted increasing attention. The mechanics of a flying soccer ball have induced abnormally higher retinal stresses and strains, and their correlation with retinal injuries has been characterized using the finite element (FE) method. However, FE simulations demand solid mechanical expertise and extensive computational time, both of which are difficult to adopt in clinical settings. This study proposes a framework that combines FE analysis with a machine learning (ML) approach for the fast prediction of retina mechanics. Different impact scenarios were simulated using the FE method to obtain the von Mises stress map and the maximum principal strain map in the posterior retina. These stress and strain patterns, along with their input parameters, were used to train and test a partial least squares regression (PLSR) model to predict the soccer-induced retina stress and strain in terms of distributions and peak magnitudes. The peak von Mises stress and maximum principal strain prediction errors were 3.03% and 9.94% for the frontal impact and were 9.08% and 16.40% for the diagonal impact, respectively. The average prediction error of von Mises stress and the maximum principal strain were 15.62% and 21.15% for frontal impacts and were 10.77% and 21.78% for diagonal impacts, respectively. This work provides a surrogate model of FE analysis for the fast prediction of the dynamic mechanics of the retina in response to the soccer impact, which could be further utilized for developing a diagnostic tool for soccer-related ocular trauma.
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Affiliation(s)
- Yasin Shokrollahi
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32901, USA; (Y.S.); (P.D.); (M.K.)
| | - Pengfei Dong
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32901, USA; (Y.S.); (P.D.); (M.K.)
| | - Mehmet Kaya
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32901, USA; (Y.S.); (P.D.); (M.K.)
| | - Donny W. Suh
- Gavin Herbert Eye Institute (GHEI), University of California at Irvine, Irvine, CA 92697, USA;
| | - Linxia Gu
- Department of Biomedical and Chemical Engineering and Sciences, Florida Institute of Technology, Melbourne, FL 32901, USA; (Y.S.); (P.D.); (M.K.)
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