An economic impact of incorrect referrals for MRI and CT scans: A retrospective analysis

Baiguissova, Dinara; Laghi, Andrea; Rakhimbekova, Assel; Fakhradiyev, Ildar; Mukhamejanova, Aigerim; Battalova, G.A.; Tanabayeva, Shynar; Zharmenov, Samat; Saliev, Timur; Каусова, Г К

doi:10.1002/hsr2.1102

Cited by 1 publication

(1 citation statement)

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“…However, it is difficult to diagnose or suspect the disease with the unaided eye of a doctor, especially during the pre-collapse stages of the disease. In this situation, doctors take two conservative and non-conservative approaches in dealing with pelvic pain, the first approach leads to unnecessary pelvic MRIs in most people, and the second approach, is based on denying patients’ symptoms and referring to the symptoms as psychosomatic symptoms, sometimes leads to missing the early stages of the disease [ 35 , 36 ].…”

Section: Discussionmentioning

confidence: 99%

Deep learning approach to femoral AVN detection in digital radiography: differentiating patients and pre-collapse stages

Rakhshankhah,

Abbaszadeh,

Kazemi

et al. 2024

BMC Musculoskelet Disord

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Objective This study aimed to evaluate a new deep-learning model for diagnosing avascular necrosis of the femoral head (AVNFH) by analyzing pelvic anteroposterior digital radiography. Methods The study sample included 1167 hips. The radiographs were independently classified into 6 stages by a radiologist using their simultaneous MRIs. After that, the radiographs were given to train and test the deep learning models of the project including SVM and ANFIS layer using the Python programming language and TensorFlow library. In the last step, the test set of hip radiographs was provided to two independent radiologists with different work experiences to compare their diagnosis performance to the deep learning models’ performance using the F1 score and Mcnemar test analysis. Results The performance of SVM for AVNFH detection (AUC = 82.88%) was slightly higher than less experienced radiologists (79.68%) and slightly lower than experienced radiologists (88.4%) without reaching significance (p-value > 0.05). Evaluation of the performance of SVM for pre-collapse AVNFH detection with an AUC of 73.58% showed significantly higher performance than less experienced radiologists (AUC = 60.70%, p-value < 0.001). On the other hand, no significant difference is noted between experienced radiologists and SVM for pre-collapse detection. ANFIS algorithm for AVNFH detection with an AUC of 86.60% showed significantly higher performance than less experienced radiologists (AUC = 79.68%, p-value = 0.04). Although reaching less performance compared to experienced radiologists statistically not significant (AUC = 88.40%, p-value = 0.20). Conclusions Our study has shed light on the remarkable capabilities of SVM and ANFIS as diagnostic tools for AVNFH detection in radiography. Their ability to achieve high accuracy with remarkable efficiency makes them promising candidates for early detection and intervention, ultimately contributing to improved patient outcomes.

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Section: Discussionmentioning

confidence: 99%