Deep-learning predicted PET can be subtracted from the true clinical fluorodeoxyglucose PET co-registered to MRI to identify the epileptogenic zone in focal epilepsy

Rheumatoid arthritis complicated with cervical actinomycosis and ureteral obstruction: A case report and literature review

Actinomycosis is a rare chronic granulomatous disease characterized by granuloma formation and tissue fibrosis with sinus tracts, often misdiagnosed due to its similarity to many infectious and non-infectious diseases. This report presents a case of a 60-year-old female with more than 10 years history of rheumatoid arthritis who developed actinomycosis infection after long-term treatment with immunosuppressants and biologics, including methotrexate, leflunomide, and infliximab, leading to recurrent joint pain, poorly controlled rheumatoid arthritis activity, and persistent elevation of white blood cell counts. Abdominal CT revealed a pelvic mass and right ureteral dilation. Pathological examination of cervical tissue showed significant neutrophil infiltration and sulfur granules, indicating actinomycosis. The patient received 18 months of doxycycline treatment for the infection and continued rheumatoid arthritis therapy with leflunomide, hydroxychloroquine sulfate, and tofacitinib, resulting in improved joint symptoms and normalized white blood cell counts. After 2 years of follow-up, the patient remained stable with no recurrence. This case highlights the importance of clinicians being vigilant for infections, particularly chronic, occult infections from rare pathogens, in rheumatoid arthritis patients on potent immunosuppressants and biologics, advocating for early screening and diagnosis.

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

PURPOSE OF REVIEW

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

RECENT FINDINGS

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.

Characterisation of a novel [18F]FDG brain PET database and combination with a second database for optimising detection of focal abnormalities, using focal cortical dysplasia as an example

BACKGROUND

Brain [18F]FDG PET is used clinically mainly in the presurgical evaluation for epilepsy surgery and in the differential diagnosis of neurodegenerative disorders. While scans are usually interpreted visually on an individual basis, comparison against normative cohorts allows statistical assessment of abnormalities and potentially higher sensitivity for detecting abnormalities. Little work has been done on out-of-sample databases (acquired differently to the patient data). Combination of different databases would potentially allow better power and discrimination. We fully characterised an unpublished healthy control brain [18F]FDG PET database (Marseille, n = 60, ages 21-78 years) and compared it to another publicly available database (MRXFDG, n = 37, ages 23-65 years). We measured and then harmonised spatial resolution and global values. A collection of patient scans (n = 34, 13-48 years) with histologically confirmed focal cortical dysplasias (FCDs) obtained on three generations of scanners was used to estimate abnormality detection rates using standard software (statistical parametric mapping, SPM12).

RESULTS

Regional SUVs showed similar patterns, but global values and resolutions were different as expected. Detection rates for the FCDs were 50% for comparison with the Marseille database and 53% for MRXFDG. Simply combining both databases worsened the detection rate to 41%. After harmonisation of spatial resolution, using a full factorial design matrix to accommodate global differences, and leaving out controls older than 60 years, we achieved detection rates of up to 71% for both databases combined. Detection rates were similar across the three scanner types used for patients, and high for patients whose MRI had been normal (n = 10/11).

CONCLUSIONS

As expected, global and regional data characteristics are database specific. However, our work shows the value of increasing database size and suggests ways in which database differences can be overcome. This may inform analysis via traditional statistics or machine learning, and clinical implementation.