Zain Al Abdeen Al Zuabi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Zain Al Abdeen Al Zuabi has a total of 32 co-authors. Here are the top co-authors by number of shared publications:
- Chandra Pasapula
The Queen Elizabeth Hospital Kings Lynn NHS Foundation Trust, King’s Lynn PE30 4ET, UK
2 shared publications A Aziz
Orthopedics, The Queen Elizabeth Hospital King's Lynn NHS Foundation Trust, King's Lynn, GBR
1 shared publicationAakanksha Shetty
Medicine and Surgery, The Queen Elizabeth Hospital King's Lynn NHS Foundation Trust, King's Lynn, GBR
1 shared publication- Aakanksha Shetty
The Queen Elizabeth Hospital Kings Lynn NHS Foundation Trust, King’s Lynn PE30 4ET, UK
1 shared publication - Adam Devany
James Paget University Hospital, Great Yarmouth NR31 6LA, UK
1 shared publication Ahmed AbdelMoety
Electrical Engineering Department, Faculty of Engineering, Aswan University, Aswan, Egypt
1 shared publication
Latest Publications
- Open Access
- 12 November 2026
- Published by MDPI AG in Journal of Clinical Medicine
- Vol. 15 (22) , 8876
Tarsal Tunnel Syndrome: A Comprehensive and Critical Review
Tarsal tunnel syndrome (TTS) was first described independently by Keck and Lam in 1962 as a compressive neuropathy of the posterior tibial nerve and its branches occurring behind the medial malleolus. Symptoms include pain, paraesthesia, and sensory disturbance in the medial and plantar aspects of the foot. The condition affects women more frequently than men and, while often idiopathic, has a range of identifiable intrinsic and extrinsic causes. Recent cadaveric studies have refined understanding of tarsal tunnel anatomy, demonstrating that the flexor retinaculum is frequently an extension of the crural fascia rather than an independent structure, and that the lateral plantar nerve and the nerve to abductor digiti minimi share a common origin in the majority of cases. Diagnosis remains challenging in the absence of a single pathognomonic test. Nerve conduction studies carry a false-negative rate approaching 50%, and quantitative ultrasound has emerged as a complementary investigation, though the evidence base for each modality varies markedly in quality and is critically appraised below rather than simply catalogued. Reported surgical success rates vary widely (44–95%), and this review argues that this variability is itself informative: it reflects heterogeneous diagnostic criteria and outcome measures across a literature composed almost entirely of retrospective, uncontrolled, single-centre case series, rather than genuine disagreement about whether surgery works. Bone–nerve contact on imaging, hindfoot malalignment, and coexistent plantar fasciitis have been associated with poorer outcomes in a single retrospective series, whereas identifiable space-occupying lesions consistently predict good results. Prognostic factors, the coexistence of TTS with diabetic neuropathy, and the emerging ‘double crush’ phenomenon are discussed, alongside an explicit assessment of where the evidence is strong, where it is weak, and where clinical practice currently outruns the data.
- 12 November 2026
- Published by MDPI AG in Journal of Clinical Medicine
- Vol. 15 (22) , 8876
Open AccessTarsal Tunnel Syndrome: A Comprehensive and Critical Review
Tarsal tunnel syndrome (TTS) was first described independently by Keck and Lam in 1962 as a compressive neuropathy of the posterior tibial nerve and its branches occurring behind the medial malleolus. Symptoms include pain, paraesthesia, and sensory disturbance in the medial and plantar aspects of the foot. The condition affects women more frequently than men and, while often idiopathic, has a range of identifiable intrinsic and extrinsic causes. Recent cadaveric studies have refined understanding of tarsal tunnel anatomy, demonstrating that the flexor retinaculum is frequently an extension of the crural fascia rather than an independent structure, and that the lateral plantar nerve and the nerve to abductor digiti minimi share a common origin in the majority of cases. Diagnosis remains challenging in the absence of a single pathognomonic test. Nerve conduction studies carry a false-negative rate approaching 50%, and quantitative ultrasound has emerged as a complementary investigation, though the evidence base for each modality varies markedly in quality and is critically appraised below rather than simply catalogued. Reported surgical success rates vary widely (44–95%), and this review argues that this variability is itself informative: it reflects heterogeneous diagnostic criteria and outcome measures across a literature composed almost entirely of retrospective, uncontrolled, single-centre case series, rather than genuine disagreement about whether surgery works. Bone–nerve contact on imaging, hindfoot malalignment, and coexistent plantar fasciitis have been associated with poorer outcomes in a single retrospective series, whereas identifiable space-occupying lesions consistently predict good results. Prognostic factors, the coexistence of TTS with diabetic neuropathy, and the emerging ‘double crush’ phenomenon are discussed, alongside an explicit assessment of where the evidence is strong, where it is weak, and where clinical practice currently outruns the data.
- Open Access
- 20 October 2026
- Published by Springer Nature in International Journal of Retina and Vitreous
Automated retinal disease classification from optical coherence tomography images using deep learning
Purpose: To develop and comparatively evaluate a reproducible Deep Learning (DL) framework for automated multiclass retinal disease classification from Optical Coherence Tomography (OCT) images in a retrospective comparative diagnostic accuracy study. Method: A publicly available OCT dataset containing 2064 images from 820 patients was used to classify seven diagnostic categories: Age-Related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), normal retina, Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO) and Vitreomacular Interface Disease (VID). To avoid data leakage, the dataset was partitioned at the patient level into training (1481 images; 592 patients), validation (281 images; 105 patients) and test (302 images; 123 patients) sets. Eight Convolutional Neural Network (CNN) architectures (InceptionV3, VGG16, MobileNetV2, AlexNet, EfficientNetV2B2, ResNet50, DenseNet121 and Xception) were trained within the same experimental pipeline using pretrained weights, weighted cross-entropy loss, Adaptive Moment Estimation with decoupled weight decay (AdamW) optimisation, early stopping and checkpointing. Performance was assessed using overall accuracy, balanced accuracy, macro Area Under the Receiver Operating Characteristic Curve (AUROC), per-class precision, recall and F1-score, confusion matrices and Expected Calibration Error (ECE), with temperature scaling applied for probability calibration. Results: All evaluated models demonstrated strong diagnostic performance on the patient-disjoint test set. Xception achieved the best overall classification results, with 93.05% accuracy and 87.39% balanced accuracy. ResNet50 and DenseNet121 also performed strongly, achieving accuracies of 91.72% and 91.39%, respectively, with macro AUROC values above 0.98. ResNet50 produced the highest macro AUROC (0.99021) and the lowest calibrated ECE (0.02171), indicating strong discrimination and reliable probability estimates. Per-class analysis showed better performance for dominant categories such as AMD and normal retina, whereas minority classes remained more challenging. Conclusions: A reproducible DL framework for OCT-based retinal disease classification was developed and shown to perform effectively across seven retinal diagnostic categories. Among the evaluated architectures, Xception provided the best overall classification performance, while ResNet50 offered the strongest discrimination and calibration. These findings support the potential of DL as a reliable tool for automated retinal disease detection from OCT images. Clinical trial number: Not applicable.
- 20 October 2026
- Published by Springer Nature in International Journal of Retina and Vitreous
Open AccessAutomated retinal disease classification from optical coherence tomography images using deep learning
Purpose: To develop and comparatively evaluate a reproducible Deep Learning (DL) framework for automated multiclass retinal disease classification from Optical Coherence Tomography (OCT) images in a retrospective comparative diagnostic accuracy study. Method: A publicly available OCT dataset containing 2064 images from 820 patients was used to classify seven diagnostic categories: Age-Related Macular Degeneration (AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), normal retina, Retinal Artery Occlusion (RAO), Retinal Vein Occlusion (RVO) and Vitreomacular Interface Disease (VID). To avoid data leakage, the dataset was partitioned at the patient level into training (1481 images; 592 patients), validation (281 images; 105 patients) and test (302 images; 123 patients) sets. Eight Convolutional Neural Network (CNN) architectures (InceptionV3, VGG16, MobileNetV2, AlexNet, EfficientNetV2B2, ResNet50, DenseNet121 and Xception) were trained within the same experimental pipeline using pretrained weights, weighted cross-entropy loss, Adaptive Moment Estimation with decoupled weight decay (AdamW) optimisation, early stopping and checkpointing. Performance was assessed using overall accuracy, balanced accuracy, macro Area Under the Receiver Operating Characteristic Curve (AUROC), per-class precision, recall and F1-score, confusion matrices and Expected Calibration Error (ECE), with temperature scaling applied for probability calibration. Results: All evaluated models demonstrated strong diagnostic performance on the patient-disjoint test set. Xception achieved the best overall classification results, with 93.05% accuracy and 87.39% balanced accuracy. ResNet50 and DenseNet121 also performed strongly, achieving accuracies of 91.72% and 91.39%, respectively, with macro AUROC values above 0.98. ResNet50 produced the highest macro AUROC (0.99021) and the lowest calibrated ECE (0.02171), indicating strong discrimination and reliable probability estimates. Per-class analysis showed better performance for dominant categories such as AMD and normal retina, whereas minority classes remained more challenging. Conclusions: A reproducible DL framework for OCT-based retinal disease classification was developed and shown to perform effectively across seven retinal diagnostic categories. Among the evaluated architectures, Xception provided the best overall classification performance, while ResNet50 offered the strongest discrimination and calibration. These findings support the potential of DL as a reliable tool for automated retinal disease detection from OCT images. Clinical trial number: Not applicable.
- Open Access
- 31 January 2026
- Published by Springer Nature in Cureus
- Vol. 18 (1)
The Role of Distal Medial Cuneiform Angle in Hallux Valgus: A Systematic Review
The obliquity of the first metatarsocuneiform joint has long been proposed as a contributing factor in the development of hallux valgus (HV). Several studies have evaluated the medial slope of the distal articular surface of the medial cuneiform relative to its longitudinal axis, commonly described as the distal medial cuneiform angle (DMCA). An increased DMCA has been hypothesized to predispose individuals to HV by altering first-ray biomechanics.
- 31 January 2026
- Published by Springer Nature in Cureus
- Vol. 18 (1)
Open AccessThe Role of Distal Medial Cuneiform Angle in Hallux Valgus: A Systematic Review
The obliquity of the first metatarsocuneiform joint has long been proposed as a contributing factor in the development of hallux valgus (HV). Several studies have evaluated the medial slope of the distal articular surface of the medial cuneiform relative to its longitudinal axis, commonly described as the distal medial cuneiform angle (DMCA). An increased DMCA has been hypothesized to predispose individuals to HV by altering first-ray biomechanics.
- Open Access
- 22 January 2026
- Published by Springer Nature in Cureus
- Vol. 18 (1)
Incidence of Midfoot Instability Associated With Medial Malleolus Fractures: A Retrospective Cohort Study
Background: The incidence of secondary medial arch instability (talonavicular and first ray instability) associated with medial malleolus fractures has not been totally quantified, and to date, its contribution to poor foot function is unknown. Methods: Twenty-five patients with various mechanisms of ankle injuries associated with medial malleolus fractures who underwent surgical treatment were assessed for patient demographics, mechanism of injury, type of fracture and quantity of medial malleolus displacement in X-rays prior to surgery, type of fixation, and level of midfoot instability, assessed by determining and quantifying talonavicular laxity and first ray instability. Results: In rotational ankle fractures with the medial malleolus involved, increased midfoot laxity and first ray instability were present in injured ankles compared to the uninjured (p0.05). There was no significant difference in the spring ligament thickness between injured and uninjured feet (p>0.05), and the displacement of the medial malleolus was not a predictor for the development of increased talonavicular laxity (p>0.05). Conclusion: Ankle fractures with medial malleolus fractures have a significantly higher incidence of medial arch instability. Rotational ankle injuries, arising from supination external rotation and pronation injuries, can significantly affect medial arch stability. We advocate that surgical restoration of superficial deltoid-spring/capsular-ligamentous integrity and/or early post-operative orthotics after medial malleolar fractures may protect the first ray destabilization and preserve future foot function.
- 22 January 2026
- Published by Springer Nature in Cureus
- Vol. 18 (1)
Open AccessIncidence of Midfoot Instability Associated With Medial Malleolus Fractures: A Retrospective Cohort Study
Background: The incidence of secondary medial arch instability (talonavicular and first ray instability) associated with medial malleolus fractures has not been totally quantified, and to date, its contribution to poor foot function is unknown. Methods: Twenty-five patients with various mechanisms of ankle injuries associated with medial malleolus fractures who underwent surgical treatment were assessed for patient demographics, mechanism of injury, type of fracture and quantity of medial malleolus displacement in X-rays prior to surgery, type of fixation, and level of midfoot instability, assessed by determining and quantifying talonavicular laxity and first ray instability. Results: In rotational ankle fractures with the medial malleolus involved, increased midfoot laxity and first ray instability were present in injured ankles compared to the uninjured (p0.05). There was no significant difference in the spring ligament thickness between injured and uninjured feet (p>0.05), and the displacement of the medial malleolus was not a predictor for the development of increased talonavicular laxity (p>0.05). Conclusion: Ankle fractures with medial malleolus fractures have a significantly higher incidence of medial arch instability. Rotational ankle injuries, arising from supination external rotation and pronation injuries, can significantly affect medial arch stability. We advocate that surgical restoration of superficial deltoid-spring/capsular-ligamentous integrity and/or early post-operative orthotics after medial malleolar fractures may protect the first ray destabilization and preserve future foot function.