Multi-parameter-based radiological diagnosis of Chiari Malformation using Machine Learning Technology.

BACKGROUND: The known primary radiological diagnosis of Chiari Malformation-I (CM-I) is based on the degree of tonsillar herniation (TH) below the Foramen Magnum (FM). However, recent data also shows the association of such malformation with smaller posterior cranial fossa (PCF) volume and the anatomical issues regarding the Odontoid. This study presents the achieved result regarding some detected potential radiological findings that may aid CM-I diagnosis using several machine learning (ML) algorithms. MATERIALS AND METHODS: Midsagittal T1-weighted MR images were collected in 241 adult patients diagnosed with CM, eleven morphometric measures of the posterior cerebral fossa were performed. Patients whose imaging was performed in the same centre and on the same device were included in the study. By matching age and gender, radiological exams of 100 clinically/radiologically proven symptomatic CM-I cases and 100 healthy controls were assessed. Eleven morphometric measures of the posterior cerebral fossa were examined using 5 designed ML algorithms. RESULTS: The mean age of patients was 29.92 ± 15.03 years. The primary presenting symptoms were headaches (62%). Syringomyelia and retrocurved-odontoid were detected in 34% and 8% of patients, respectively. All of the morphometric measures were significantly different between the groups, except for the distance from the dens axis to the posterior margin of FM. The Radom Forest model is found to have the best 1.0 (14 of 14) ratio of accuracy in regard to 14 different combinations of morphometric features. CONCLUSION: Our study indicates the potential usefulness of ML-guided PCF measurements, other than TH, that may be used to predict and diagnose CM-I accurately. Combining two or three preferable osseous structure-based measurements may increase the accuracy of radiological diagnosis of CM-I.

Does the COVID-19 pandemic cause late diagnosis and delay in treatment in developmental dysplasia of hip patients?

BACKGROUND: The aim of the current study was to search the effect of COVID-19 restriction on developmental dysplasia of hip (DDH) screening. MATERIAL AND METHODS: We retrospectively reviewed the patients who brought to DDH screening in April-May and June-July 2020 and compared with the same period of 2019. We recorded age, gender, DDH type and risk factors of the patients. RESULTS: The number of patients taken for DDH screening was 430 and 400 in April-May 2019 and June-July 2019, respectively. In 2020, the number of patients taken for DDH screening was 159 and 776 in the same period, respectively. Thirteen patients were diagnosed with DDH older than 3 months age in June-July 2020. There were only two patients in same period in 2019. In 2020, 6 of 13 patients who applied to the hospital late for the routine US and had pathologic hips were not taken to orthopaedics or follow-up by their families. CONCLUSION: In the era of COVID-19, the number of late diagnosis and lost follow-up for DDH are increased. To prevent future morbidities and reduce surgical interventions, special measures should be taken.