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 3 publications

(1 citation statement)

References 25 publications

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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%

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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Overview of Therapeutic Ultrasound Applications and Safety Considerations: 2024 Update

Bader,

Padilla,

Haworth

et al. 2024

J of Ultrasound Medicine

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A 2012 review of therapeutic ultrasound was published to educate researchers and physicians on potential applications and concerns for unintended bioeffects (doi: 10.7863/jum.2012.31.4.623). This review serves as an update to the parent article, highlighting advances in therapeutic ultrasound over the past 12 years. In addition to general mechanisms for bioeffects produced by therapeutic ultrasound, current applications, and the pre‐clinical and clinical stages are outlined. An overview is provided for image guidance methods to monitor and assess treatment progress. Finally, other topics relevant for the translation of therapeutic ultrasound are discussed, including computational modeling, tissue‐mimicking phantoms, and quality assurance protocols.

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Is Magnetic Resonance Imaging Overused Among Patients Undergoing Total Knee Arthroplasty?

Mukkamala,

Schaffer,

Weber

et al. 2024

JAAOS Glob Res Rev

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Introduction: With increasing healthcare costs, it is important to quantify the number of total knee arthroplasty (TKA) patients who have MRIs that are not considered clinically necessary. The purpose of this study was to determine the number of unnecessary preconsultation MRIs done among TKA patients at our institution. Methods: Eight hundred and sixty-nine patients who underwent a primary TKA were identified. Review of medical records and imaging results was conducted to determine which patients had received preconsult MRIs and whether they were necessary or unnecessary. Results: Of the 869 TKA, 177 (20.4%) presented with a preconsultation MRI, of which, 112 met the study inclusion criteria. Of the 112 MRIs, 18 (20.7%) were done without radiographic imaging, and 69 (79.3%) were completed after radiographically evident moderate-to-severe arthritis. Overall, 87 MRIs (10.0%) were deemed clinically unnecessary. Conclusions: Referring physicians are overusing MRIs before consultation with an orthopaedic surgeon. The 87 patients who had unnecessary MRIs at our institution over a one-year period represented a cost of $20,706. Extrapolating that number to the scale of patients affected by arthritis each year is potentially a staggering amount of money. Evidence-based guidelines should be put into place to optimize healthcare utilization.

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

Cited by 3 publications

References 25 publications

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

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

Overview of Therapeutic Ultrasound Applications and Safety Considerations: 2024 Update

Is Magnetic Resonance Imaging Overused Among Patients Undergoing Total Knee Arthroplasty?

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