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Siwetz M, Widni-Pajank H, Hammer N, Pilsl U, Bruneder S, Wree A, Antipova V. The Course and Variation of the Facial Vein in the Face-Known and Unknown Facts: An Anatomical Study. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:1479. [PMID: 37629769 PMCID: PMC10456631 DOI: 10.3390/medicina59081479] [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: 07/13/2023] [Revised: 08/10/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023]
Abstract
Background and Objectives: The facial vein is the main collector of venous blood from the face. It plays an important role in physiological as well as pathological context. However, to date, only limited data on the course and tributaries of the facial vein are present in contemporary literature. The aim of this study was to provide detail on the course and the tributaries of the facial vein. Materials and Methods: In 96 sides of 53 body donors, latex was injected into the facial vein. Dissection was carried out and the facial vein and its tributaries (angular vein, ophthalmic vein, nasal veins, labial veins, palpebral veins, buccal and masseteric veins) were assessed. Results: The facial vein presented a textbook-like course in all cases and crossed the margin of the mandible anterior to the masseter in 6.8% of cases, while being located deep to the zygomaticus major muscle in all cases and deep to the zygomaticus minor in 94.6% of cases. Conclusions: This work offers detailed information on the course of the facial vein in relation to neighboring structures, which shows a relatively consistent pattern, as well as on its tributaries, which show a high variability.
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Affiliation(s)
- Martin Siwetz
- Division of Macroscopic and Clinical Anatomy, Gottfried Schatz Research Center, Medical University of Graz, Auenbruggerplatz 25, A-8036 Graz, Austria
| | - Hannes Widni-Pajank
- Division of Macroscopic and Clinical Anatomy, Gottfried Schatz Research Center, Medical University of Graz, Auenbruggerplatz 25, A-8036 Graz, Austria
- Department of Oral and Maxillofacial Surgery, Klagenfurt Am Wörthersee Clinic, Feschnigstraße 11, A-9020 Klagenfurt am Wörthersee, Austria
| | - Niels Hammer
- Division of Macroscopic and Clinical Anatomy, Gottfried Schatz Research Center, Medical University of Graz, Auenbruggerplatz 25, A-8036 Graz, Austria
- Department of Orthopedic and Trauma Surgery, University of Leipzig, D-04103 Leipzig, Germany
- Division of Biomechatronics, Fraunhofer Institute for Machine Tools and Forming Technology Dresden, D-09126 Dresden, Germany
| | - Ulrike Pilsl
- Division of Macroscopic and Clinical Anatomy, Gottfried Schatz Research Center, Medical University of Graz, Auenbruggerplatz 25, A-8036 Graz, Austria
| | - Simon Bruneder
- Department of Oral and Maxillofacial Surgery, Medical University of Graz, Auenbruggerplatz 5, A-8036 Graz, Austria
| | - Andreas Wree
- Institute of Anatomy, Rostock University Medical Center, Gertrudenstr. 9, D-18057 Rostock, Germany
| | - Veronica Antipova
- Division of Macroscopic and Clinical Anatomy, Gottfried Schatz Research Center, Medical University of Graz, Auenbruggerplatz 25, A-8036 Graz, Austria
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Analysis of facial ultrasonography images based on deep learning. Sci Rep 2022; 12:16480. [PMID: 36182939 PMCID: PMC9526737 DOI: 10.1038/s41598-022-20969-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 09/21/2022] [Indexed: 11/28/2022] Open
Abstract
Transfer learning using a pre-trained model with the ImageNet database is frequently used when obtaining large datasets in the medical imaging field is challenging. We tried to estimate the value of deep learning for facial US images by assessing the classification performance for facial US images through transfer learning using current representative deep learning models and analyzing the classification criteria. For this clinical study, we recruited 86 individuals from whom we acquired ultrasound images of nine facial regions. To classify these facial regions, 15 deep learning models were trained using augmented or non-augmented datasets and their performance was evaluated. The F-measure scores average of all models was about 93% regardless of augmentation in the dataset, and the best performing model was the classic model VGGs. The models regarded the contours of skin and bones, rather than muscles and blood vessels, as distinct features for distinguishing regions in the facial US images. The results of this study can be used as reference data for future deep learning research on facial US images and content development.
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An Orbital Venous Varix Presenting as a Strain-Induced Lower Eyelid Mass. J Craniofac Surg 2021; 32:e562-e563. [PMID: 34516063 DOI: 10.1097/scs.0000000000007664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
ABSTRACT Orbital varix is uncommon disease entity, accounting for less than 1% of orbital tumor. Authors report a case of tumor mimicking lower eyelid varix of inferior palpebral vein induced by forced closure of the patient's eyelids. A 21-year-old female visited our institution with a complaint of eyelid mass that only appeared on frowning. A 0.5 × 1.0 cm2 sized soft, nontender and nonpulsating mass was observed at her left lower eyelid when she frowned. Preoperative ultrasound imaging revealed a hypoechoic cystic lesion above orbicularis oculi muscle. A surgical resection through transconjunctival approach was performed. Congestion of perforating inferior palpebral vein caused by contraction of orbicularis oculi muscle was observed intraoperatively. Histopathology has confirmed dilated venous structures. The symptom was immediately resolved after surgery. No sign of recurrence was detected after two years of follow-up.
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Singh RB, Liu L, Anchouche S, Yung A, Mittal SK, Blanco T, Dohlman TH, Yin J, Dana R. Ocular redness - I: Etiology, pathogenesis, and assessment of conjunctival hyperemia. Ocul Surf 2021; 21:134-144. [PMID: 34010701 PMCID: PMC8328962 DOI: 10.1016/j.jtos.2021.05.003] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 01/01/2023]
Abstract
The translucent appearance of the conjunctiva allows for immediate visualization of changes in the circulation of the conjunctival microvasculature consisting of extensive branching of superficial and deep arterial systems and corresponding drainage pathways, and the translucent appearance of the conjunctiva allows for immediate visualization of changes in the circulation. Conjunctival hyperemia is caused by a pathological vasodilatory response of the microvasculature in response to inflammation due to a myriad of infectious and non-infectious etiologies. It is one of the most common contributors of ocular complaints that prompts visits to medical centers. Our understanding of these neurogenic and immune-mediated pathways has progressed over time and has played a critical role in developing targeted novel therapies. Due to a multitude of underlying etiologies, patients must be accurately diagnosed for efficacious management of conjunctival hyperemia. The diagnostic techniques used for the grading of conjunctival hyperemia have also evolved from descriptive and subjective grading scales to more reliable computer-based objective grading scales.
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Affiliation(s)
- Rohan Bir Singh
- Laboratory of Corneal Immunology, Transplantation and Regeneration, Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Lingjia Liu
- Laboratory of Corneal Immunology, Transplantation and Regeneration, Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Sonia Anchouche
- Laboratory of Corneal Immunology, Transplantation and Regeneration, Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA; Faculty of Medicine, McGill University, Montreal, Quebec, Canada
| | - Ann Yung
- Laboratory of Corneal Immunology, Transplantation and Regeneration, Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Sharad K Mittal
- Laboratory of Corneal Immunology, Transplantation and Regeneration, Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Tomas Blanco
- Laboratory of Corneal Immunology, Transplantation and Regeneration, Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Thomas H Dohlman
- Laboratory of Corneal Immunology, Transplantation and Regeneration, Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Jia Yin
- Laboratory of Corneal Immunology, Transplantation and Regeneration, Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
| | - Reza Dana
- Laboratory of Corneal Immunology, Transplantation and Regeneration, Schepens Eye Research Institute, Massachusetts Eye and Ear, Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
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