Diseases and Abnormalities at or Before Birth

All Conditions

Wounds and Injuries

Symptoms and General Pathology

Neoplasms

Nervous System Diseases

Musculoskeletal Diseases

Eye Diseases

Mouth and Tooth Diseases

Congenital Abnormalities

Fractures, Bone

Facies

Cysts

Orbital Fractures

Orbital Neoplasms

Zygomatic Fractures

Maxillary Neoplasms

Maxillofacial Injuries

Facial Injuries

Craniocerebral Trauma

Trauma, Nervous System

Skull Fractures

Jaw Neoplasms

Skull Neoplasms

Bone Neoplasms

Bone Diseases

Jaw Diseases

Maxillary Diseases

Stomatognathic Diseases

Eye Neoplasms

Orbital Diseases

(reconstruction with patient specific titanium implant)

(reconstruction with preformed titanium plate preoperatively bended on stereolithographic model)

orbital defects reconstruction will be virtually planned by software and a patient specific titanium implant will be manufactured using milling technique depending on CAD-CAM technology

orbital bone reconstruction with patient specific titanium implant

orbital defects reconstruction will be virtually planned by software and a reconstructed stereolithographic model will be 3D printed and used for preoperatively bending of preformed titanium implant.

orbital bone reconstruction with preformed titanium plate preoperatively bended on stereolithographic model

Hatem A Abo El Hassan El Shazly, MMed

Sohag F Medicine

Kamal A Hassanein, Professor

Tarek A Ftohy, Ass. prof

Islam AA Amer, Ass. prof

Reconstruction of orbital defects resulting after trauma; tumor resection; maxillary cyst; craniofacial anomalies and sequestrated bone.... etc., has been a challenging issue over the years and this owing to the complicated anatomy of orbit. Inaccurate orbital reconstruction may lead to devastating cosmetic and functional complications. Titanium mesh for orbital reconstruction has now become "probably" the most popular material for orbital wall reconstruction worldwide. Innovation of CAD-CAM technology and its application in maxillofacial surgery will markedly improve the surgical outcome. This study will assess the accuracy of orbital reconstruction using CAD-CAM technology and to compare two different modalities for orbital reconstruction based on this technology.

This study will be a prospective cohort study. It will include patients treated with the custom-made implants for orbital blow out fractures, ZMC fractures or tumors. The sample will consist of 34 patients who will need orbital reconstruction and will be operated in Maxillofacial, Head and Neck Unit, General Surgery Department, Faculty Of Medicine, Sohag University, Egypt. The patients will be randomly divided into two groups, group (A) reconstructed with patient specific implant and group (B) reconstructed with preformed plate preoperatively bended on stereolithographic model. The reconstructive outcomes are assessed by clinical and ophthalmological examinations and accuracy analysis by comparing the preoperative and postoperative multi slice computed tomography data. All patients will be assessed clinically, ophthalmologically, and radiographically using computed tomography (CT) then virtual surgical planning and custom-made patient implant will be manufactured. Post-operative clinical evaluation will be done, and radiographic evaluation of all patients will be done by CT scan within one week and 6 months after the procedure. comparing data of the two groups will be done using appropriate methods.

the participants are assigned to two groups randomly A and B and receive two different intervention modality throughout the study and the results will be compared between the two groups

Application of CAD-CAM Technology in Orbital Bone Reconstruction

Comparison of Two Different Modalities for Orbital Reconstruction Based on CAD-CAM Technology: A Clinical Randomized Trial

assessment of motility of eye globe using extraocular muscle function test

cost of the patient specific implant and the cost of the stereolithographic model and preformed titanium implant

implant displacement, bleeding, infection, pain

assessment of the patients satisfaction to the results post operative using scoring system from 1 to 5 in which :

* 1 = Excellent
* 2 = good
* 3 = Fair
* 4 = not bad
* 5 = poor

testing of visual acuity of the affected eye pre and post operative

assessment of changes in cosmetic results following surgery by one of the supervisors using a scoring system from 1 to 4 in which:

* 1 = Excellent
* 2 = good
* 3 = Fair
* 4 = poor

pre and post operative reconstructed orbit volume will be measured using multi slice computed tomography (MSCT) image loaded on software and compared with preoperative and with healthy non affected orbit

pre and post operative measurement of globe position within the bony orbit using MSCT image loaded on software and comparing measures with preoperative and with healthy non affected orbit

in patients with associated ZMC affection, symmetry with the healthy side and prominence of the zygomatic eminence will be measured using MSCT image loaded on software and compared with preoperative and with healthy non affected ZMC

Hatem Adel Aboelhassan

specialist of maxillofacial, head and neck surgery

Conjunctival Neoplasms

Conjunctival Diseases

Develop an intelligent screening system for ocular surface malignant tumors, apply it to the mobile terminal for real-world verification and large-scale general screening, and test its effect on assisting doctors in the diagnosis and treatment of rare diseases.

screening system for ocular surface malignant tumors

Haotian Lin, M.D., Ph.D

Ruixin Wang, M.D., Ph.D

Rare diseases generally refer to diseases whose prevalence rate is lower than 1 / 10 000 and the number of patients is less than 140000. Rare diseases are generally faced with the dilemma of a lack of qualified doctors, difficulty in large-scale screening, and a lack of rapid and effective channels for medical treatment. Studies have shown that 42% of patients say they have been misdiagnosed, and each patient with a rare disease needs to go through an average of eight doctors in seven years to see a corresponding rare disease specialist. More importantly, most rare diseases seriously affect the health and quality of life of patients. The ocular surface malignant tumor is a typical rare disease, and its incidence is less than 1 / 100000. The ocular surface not only affects the patient's appearance, but also damages the visual function, and the malignant tumor may even affect life. These uncommon malignant tumors are often hidden in the common black nevus on the eye surface, which is easy to be ignored and has great potential risks. With the improvement of people's living standards, people start to pay attention to rare diseases.

In recent years, the rapid development of digital technology has also provided new opportunities for the prevention and treatment of rare diseases. Our team established the database of rare ophthalmopathy in China in the early stage, which provided a solid foundation for the digitization of precious clinical data. This study intends to develop an intelligent screening system for ocular surface malignant tumors, using the mobile phone for real-world verification and scale screening, and explore it to improve the ability of doctors to diagnose and treat rare diseases. This study is expected to improve the ability to screen malignant tumors on the ocular surface and provide a novel model for the universal screening of rare diseases.

Artificial Intelligence-assisted Screening of Malignant Pigmented Tumors on the Ocular Surface

Measure of the ability of a binary classifier to distinguish between malignent and benign.

The study will assess the sensitivity and specificity of the CaptureTumor (CaT) system under various conditions.

Count the number of people who have successfully received and read knowledge about ocular surface pigmented tumors on each offline and online platform.

For cases where the system judges that it is necessary to go to the hospital for further diagnosis and treatment, two or more researchers will conduct a diagnostic review first. If further diagnosis and treatment is really needed, the subject will be contacted and told to go to the hospital for treatment by phone, text message, etc., and continue to follow up. Finally, the duration of diagnosis (screening time to pathological diagnosis time), visit distance, number of visits before diagnosis, and the proportion of referred patients in all subjects were counted.

Doctors with different seniority were asked to diagnose the test set with and without assistance from the intelligent screening system, and the accuracy in the two cases were calculated and compared.

Sun Yat-sen University

Clinical Professor

Patients with malignant orbital tumors (lymphoma, melanoma, ...) diagnosed by pathological confirmation.

Patients with benign orbital tumors (cavernous hemangioma, inflammatory pseudotumor, ...) diagnosed by pathological confirmation.

Diagnosis models are established using quantitative features extracted from the multi-parametric MRI images and further processed by appropriate deep learning or machine learning algorithms.

Multi-parametric MRI and image analysis by deep learning or machine learning algorithms

Junfang Xian, M.D., Ph.D.

Orbital tumors can be categorized into benign and malignant tumors, and there are significant variations in their biological behavior, treatment, and prognosis. This study aims to enhance the accurate diagnosis and risk stratification of orbital tumors using artificial intelligence (AI) technology and multiparameter magnetic resonance imaging (MRI) data. It further explores the intrinsic relationship between MRI and the differential diagnosis of benign and malignant orbital tumors, as well as the pathological subtypes of malignant tumors and Ki-67 expression levels. This research aims to aid in guiding personalized diagnosis and treatment decision-making for patients with orbital tumors while promoting the practical application and incorporation of AI technology.

Although orbital tumors are less common than other eye-related diseases, they can be extremely detrimental to patients. Not only can they cause physical disfigurement, but they can also lead to functional impairments such as diminished vision and restricted eye movement. Orbital tumors can be categorized as either benign or malignant, and there are significant disparities in their biological behavior, treatment approaches, outcomes, and prognosis, which complicates the processes of differential diagnosis and treatment selection. For malignant lesions, the treatment plans and prognosis of patients vary due to the different pathological types and stages. Hence, there is a pressing clinical necessity to devise accurate diagnostic methods for orbital tumors. Multiparametric magnetic resonance imaging (mp-MRI) currently stands as the leading non-invasive imaging technique for diagnosing orbital tumors. This study is centered on precise diagnosis of orbital tumor risk stratification, utilizing artificial intelligence algorithm technology to explore the inherent connection between MRI images and the distinguishing diagnosis of benign and malignant orbital tumors, histological types and Ki-67 expression levels of malignant tumors. It aims to integrate clinical information and quantitative MRI features to construct prediction models, aid in guiding individual diagnosis and treatment decisions for patients with orbital tumors and facilitate the application and advancement of artificial intelligence technology. Specifically, the research objectives are outlined as follows:

1. Establishing a deep learning-based automatic segmentation model for orbital tumors using a multi-sequence MRI dataset from multiple centers, thereby reducing the time required for manual delineation and proving beneficial for subsequent analysis.
2. Developing a model for identifying malignant and benign orbital tumors using multiple machine learning algorithms combined with multi-sequence MRI dataset, with the aim of providing more precise information for distinguishing between these two entities.
3. Constructing robust diagnostic models using machine learning or deep learning approaches with quantitative multi-sequence MRI features to identify the histological type and Ki-67 expression levels of malignant orbital tumors, with the purpose of enhancing detection rates and accuracy, thereby achieving risk stratification for patients with malignant orbital tumors.

Risk Stratification of Orbital Tumors Based on MRl and Artificial Intelligence

The area under the ROC curve is calculated by integrating the ROC curve, which plots Sensitivity against 1 - Specificity.

The area under the precision-recall curve is determined by integrating the Precision-Recall curve, which plots Precision against Recall.

Sensitivity is calculated as the ratio of true positives to the sum of true positives and false negatives.

Specificity is calculated as the ratio of true negatives to the sum of true negatives and false positives.

Accuracy is calculated as the ratio of the sum of true positives and true negatives to the total number of cases.

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization
Application of CAD-CAM Technology in Orbital Bone Reconstruction	NCT05438784	Orbital Bone Fr... Maxillary Cyst Maxillary Neopl... Zygomatic Fract... Orbital Neoplas... Facial Deformit...	orbital bone re... orbital bone re...	16 Years -	Sohag University
Application of CAD-CAM Technology in Orbital Bone Reconstruction	NCT05438784	Orbital Bone Fr... Maxillary Cyst Maxillary Neopl... Zygomatic Fract... Orbital Neoplas... Facial Deformit...	orbital bone re... orbital bone re...	16 Years -	Sohag University
Artificial Intelligence-assisted Screening of Malignant Pigmented Tumors on the Ocular Surface	NCT05645341	Orbital Neoplas... Conjunctival Ne... Eye Neoplasms	screening syste...	-	Sun Yat-sen University
Risk Stratification of Orbital Tumors Based on MRl and Artificial Intelligence	NCT06336499	Orbital Neoplas...	Multi-parametri...	18 Years -	Beijing Tongren Hospital

Spots Global Cancer Trial Database for orbital neoplasms