Introduction to The Spine Journal special issue on artificial intelligence and machine learning

The Promise of AI for Image-Driven Medicine: Qualitative Interview Study of Radiologists' and Pathologists' Perspectives

Background

Image-driven specialisms such as radiology and pathology are at the forefront of medical artificial intelligence (AI) innovation. Many believe that AI will lead to significant shifts in professional roles, so it is vital to investigate how professionals view the pending changes that AI innovation will initiate and incorporate their views in ongoing AI developments.

Objective

Our study aimed to gain insights into the perspectives and wishes of radiologists and pathologists regarding the promise of AI.

Methods

We have conducted the first qualitative interview study investigating the perspectives of both radiologists and pathologists regarding the integration of AI in their fields. The study design is in accordance with the consolidated criteria for reporting qualitative research (COREQ).

Results

In total, 21 participants were interviewed for this study (7 pathologists, 10 radiologists, and 4 computer scientists). The interviews revealed a diverse range of perspectives on the impact of AI. Respondents discussed various task-specific benefits of AI; yet, both pathologists and radiologists agreed that AI had yet to live up to its hype. Overall, our study shows that AI could facilitate welcome changes in the workflows of image-driven professionals and eventually lead to better quality of care. At the same time, these professionals also admitted that many hopes and expectations for AI were unlikely to become a reality in the next decade.

Conclusions

This study points to the importance of maintaining a "healthy skepticism" on the promise of AI in imaging specialisms and argues for more structural and inclusive discussions about whether AI is the right technology to solve current problems encountered in daily clinical practice.

Prevalence, diagnosis, and impact on clinical outcomes of dural ossification in the thoracic ossification of the ligamentum flavum: a systematic review

STUDY DESIGN

Systematic review.

BACKGROUND CONTEXT

Thoracic ossification of the ligamentum flavum (TOLF) has become the principal cause of thoracic spinal stenosis. Dural ossification (DO) was a common clinical feature accompanying with TOLF. However, on account of the rarity, we know little about the DO in TOLF so far.

PURPOSE

This study was conducted to elucidate the prevalence, diagnostic measures, and impact on the clinical outcomes of DO in TOLF by integrating the existing evidence.

METHODS

PubMed, Embase, and Cochrane Database were comprehensively searched for studies relevant to the prevalence, diagnostic measures, or impact on the clinical outcomes of DO in TOLF. All retrieved studies meeting the inclusion and criterion were included into this systematic review.

RESULTS

The prevalence of DO in TOLF treated surgically was 27% (281/1046), ranging from 11 to 67%. Eight diagnostic measures have been put forward to predict the DO in TOLF using the CT or MRI modalities, including "tram track sign", "comma sign", "bridge sign", "banner cloud sign", "T2 ring sign", TOLF-DO grading system, CSAOR grading system, and CCAR grading system. DO did not affect the neurological recovery of TOLF patients treated with the laminectomy. The rate of dural tear or CSF leakage in TOLF patients with DO was approximately 83% (149/180).

CONCLUSION

The prevalence of DO in TOLF treated surgically was 27%. Eight diagnostic measures have been put forward to predict the DO in TOLF. DO did not affect the neurological recovery of TOLF treated with laminectomy but was associated with high risk of complications.

Evaluation of Deep Learning-Based Automated Detection of Primary Spine Tumors on MRI Using the Turing Test

Background

Recently, the Turing test has been used to investigate whether machines have intelligence similar to humans. Our study aimed to assess the ability of an artificial intelligence (AI) system for spine tumor detection using the Turing test.

Methods

Our retrospective study data included 12179 images from 321 patients for developing AI detection systems and 6635 images from 187 patients for the Turing test. We utilized a deep learning-based tumor detection system with Faster R-CNN architecture, which generates region proposals by Region Proposal Network in the first stage and corrects the position and the size of the bounding box of the lesion area in the second stage. Each choice question featured four bounding boxes enclosing an identical tumor. Three were detected by the proposed deep learning model, whereas the other was annotated by a doctor; the results were shown to six doctors as respondents. If the respondent did not correctly identify the image annotated by a human, his answer was considered a misclassification. If all misclassification rates were >30%, the respondents were considered unable to distinguish the AI-detected tumor from the human-annotated one, which indicated that the AI system passed the Turing test.

Results

The average misclassification rates in the Turing test were 51.2% (95% CI: 45.7%–57.5%) in the axial view (maximum of 62%, minimum of 44%) and 44.5% (95% CI: 38.2%–51.8%) in the sagittal view (maximum of 59%, minimum of 36%). The misclassification rates of all six respondents were >30%; therefore, our AI system passed the Turing test.

Conclusion

Our proposed intelligent spine tumor detection system has a similar detection ability to annotation doctors and may be an efficient tool to assist radiologists or orthopedists in primary spine tumor detection.