Decompression of Paralabral Cyst near Axillary Nerve: A Case Report.

Introduction: Paralabral cyst is benign fluid-filled lesion that occurs adjacent to glenoid labrum. Origin of the cyst can be traumatic or atraumatic. This cystic lesion can compress nearby axillary nerve or suprascapular nerve, resulting in shoulder pain and numbness. In this case report, we will discuss about anteroinferior paralabral cyst with axillary neuropathy in atraumatic condition. Case Report: A 35-year-old male was admitted in our institute with complaining of numbness in the mid-part of the lateral arm and pain in the posterior aspect in the left shoulder for 2 weeks. The patient has on-and-off pain in the left shoulder on lifting weight. He had no history of trauma. X-ray was normal. On examination, tenderness presents over the dorsal aspect of shoulder and reduced sensations over deltoid muscle (regimen badge sign). Deltoid atrophy was noted. Range of motion was normal. On examination, cervical spine was normal, and reduced sensation over the lateral aspect of arm and deltoid atrophy was present. Magnetic resonance imaging (MRI) shows large multiloculated paralabral cyst caudal to inferior glenoid rim. The diagnosis was compressive axillary neuropathy which was confirmed by nerve condition study. Conclusion: According to this case report, accurate early clinical examination and MRI evaluation are crucial in patients with atraumatic shoulder pain associated with neurological symptoms. On identification, cyst can be successfully decompressed by shoulder arthroscopy which can prevent axillary nerve damage, muscle denervation, and also recurrence of cyst can be avoided.

Neglected Metatarsophalangeal Joint Dislocation of the Lesser Toe with Associated Metatarsal Fracture Malunion in a 4-Year-Old Child: A Case Report.

CASE: A 4-year-old girl presented with a 5-week-old, neglected fourth metatarsophalangeal (MTP) joint dislocation with malunion of the fourth metatarsal. A previous attempt at closed reduction had failed. The reduction was hindered by dynamic stabilizing of soft tissues around the MTP joint. Open reduction of the fourth MTP joint dislocation and corrective osteotomy of the fourth metatarsal was performed. The patient was pain-free without any cosmetic deformity at the 1-year follow-up. CONCLUSION: Prompt recognition of a MTP dislocation is vital. The long extensor tendon to the toe can hinder the closed reduction of the MTP dislocation. Osteotomy of the metatarsal malunion is necessary for stable reduction. LEVEL OF EVIDENCE: 4.

Knee Implant Identification by Fine-Tuning Deep Learning Models.

BACKGROUND: Identification of implant model from primary knee arthroplasty in pre-op planning of revision surgery is a challenging task with added delay. The direct impact of this inability to identify the implants in time leads to the increase in complexity in surgery. Deep learning in the medical field for diagnosis has shown promising results in getting better with every iteration. This study aims to find an optimal solution for the problem of identification of make and model of knee arthroplasty prosthesis using automated deep learning models. METHODS: Deep learning algorithms were used to classify knee arthroplasty implant models. The training, validation and test comprised of 1078 radiographs with a total of 6 knee arthroplasty implant models with anterior-posterior (AP) and lateral views. The performance of the model was calculated using accuracy, sensitivity, and area under the receiver-operating characteristic curve (AUC), which were compared against multiple models trained for comparative in-depth analysis with saliency maps for visualization. RESULTS: After training for a total of 30 epochs on all 6 models, the model performing the best obtained an accuracy of 96.38%, the sensitivity of 97.2% and AUC of 0.985 on an external testing dataset consisting of 162 radiographs. The best performing model correctly and uniquely identified the implants which could be visualized using saliency maps. CONCLUSION: Deep learning models can be used to differentiate between 6 knee arthroplasty implant models. Saliency maps give us a better understanding of which regions the model is focusing on while predicting the results.