In Acta neurochirurgica ; h5-index 35.0

BACKGROUND : Hyperselective neurectomy is a reliable treatment for spasticity. This research was designed to quantify the surgical parameters of hyperselective neurectomy of thoracodorsal nerve for shoulder spasticity through anatomical studies, as well as to retrospectively assess patients who underwent this procedure to provide an objective basis for clinical practice.

METHODS : On nine embalmed adult cadavers (18 shoulders), we dissected and observed the branching patterns of thoracodorsal nerve, counted the number of nerve branches, measured the distribution of branch origin point, and determined the length of the surgical incision. Next, we selected five patients who underwent this procedure for shoulder spasticity and retrospectively evaluated (ethic committee: 2022-37) their shoulder function with active/passive range of motion (AROM/PROM) and modified Ashworth scale (MAS).

RESULTS : The anatomical study revealed that the main trunk of thoracodorsal nerve sends out one to three medial branches, with the pattern of only one medial branch being the most common (61.1%); there were significant variations in the branch numbers and nerve distributions; the location of thoracodorsal nerve branches' entry points into the muscle varied from 27.2 to 67.8% of the length of the arm. Clinical follow-up data showed significant improvement in shoulder mobility in all patients. AROM of shoulder abduction increased by 39.4° and PROM increased by 64.2° (P < 0.05). AROM and PROM of shoulder flexion increased by 36.6° and 54.4°, respectively (P < 0.05). In addition, the MAS of shoulder abduction (1.8) and flexion (1.2) was both significantly reduced in all patients (P < 0.05).

CONCLUSION : Hyperselective neurectomy of thoracodorsal nerve is effective and stable in the treatment of shoulder spasticity. Intraoperative attention is required to the numbers of the medial branch of thoracodorsal nerve. We recommend an incision in the mid-axillary line that extends from 25 to 70% of the arm length to fully expose each branch.

Lin Weishan, Li Tie, Qi Wenjun, Shen Yundong, Xu Wendong

2023-Mar-21

Hyperselective neurectomy, Latissimus dorsi, Shoulder joint, Spasticity, Thoracodorsal nerve

In Current opinion in ophthalmology

PURPOSE OF REVIEW : Age-related macular degeneration (AMD) is one of the leading causes of blindness and can progress to geographic atrophy (GA) in late stages of disease. This review article highlights recent literature which assists in the accurate and timely identification of GA, and monitoring of GA progression.

RECENT FINDINGS : Technology for diagnosing and monitoring GA has made significant advances in recent years, particularly regarding the use of optical coherence tomography (OCT). Identification of imaging features which may herald the development of GA or its progression is critical. Deep learning applications for OCT in AMD have shown promising growth over the past several years, but more prospective studies are needed to demonstrate generalizability and clinical utility.

SUMMARY : Identification of GA and of risk factors for GA development or progression is essential when counseling AMD patients and discussing prognosis. With new therapies on the horizon for the treatment of GA, identification of risk factors for the development and progression of GA will become critical in determining the patients who would be appropriate candidates for new targeted therapies.

Clevenger Leanne, Rachitskaya Aleksandra

2023-Mar-20

In Skeletal radiology

OBJECTIVE : To compare the image quality and agreement among conventional and accelerated periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) MRI with both conventional reconstruction (CR) and deep learning-based reconstruction (DLR) methods for evaluation of shoulder.

MATERIALS AND METHODS : We included patients who underwent conventional (acquisition time, 8 min) and accelerated (acquisition time, 4 min and 24 s; 45% reduction) PROPELLER shoulder MRI using both CR and DLR methods between February 2021 and February 2022 on a 3 T MRI system. Quantitative evaluation was performed by calculating the signal-to-noise ratio (SNR). Two musculoskeletal radiologists compared the image quality using conventional sequence with CR as the reference standard. Interobserver agreement between image sets for evaluating shoulder was analyzed using weighted/unweighted kappa statistics.

RESULTS : Ninety-two patients with 100 shoulder MRI scans were included. Conventional sequence with DLR had the highest SNR (P < .001), followed by accelerated sequence with DLR, conventional sequence with CR, and accelerated sequence with CR. Comparison of image quality by both readers revealed that conventional sequence with DLR (P = .003 and P < .001) and accelerated sequence with DLR (P = .016 and P < .001) had better image quality than the conventional sequence with CR. Interobserver agreement was substantial to almost perfect for detecting shoulder abnormalities (κ = 0.600-0.884). Agreement between the image sets was substantial to almost perfect (κ = 0.691-1).

CONCLUSION : Accelerated PROPELLER with DLR showed even better image quality than conventional PROPELLER with CR and interobserver agreement for shoulder pathologies comparable to that of conventional PROPELLER with CR, despite the shorter scan time.

Hahn Seok, Yi Jisook, Lee Ho-Joon, Lee Yedaun, Lee Joonsung, Wang Xinzeng, Fung Maggie

2023-Mar-21

Acceleration, Deep learning, Magnetic resonance imaging, Shoulder

In Bioinformatics (Oxford, England)

MOTIVATION : Deep learning attained excellent results in Digital Pathology recently. A challenge with its use is that high quality, representative training data sets are required to build robust models. Data annotation in the domain is labor intensive and demands substantial time commitment from expert pathologists. Active Learning (AL) is a strategy to minimize annotation. The goal is to select samples from the pool of unlabeled data for annotation that improves model accuracy. However, AL is a very compute demanding approach. The benefits for model learning may vary according to the strategy used, and it may be hard for a domain specialist to fine tune the solution without an integrated interface.

RESULTS : We developed a framework that includes a friendly user interface along with run-time optimizations to reduce annotation and execution time in AL in digital pathology. Our solution implements several AL strategies along with our Diversity-Aware Data Acquisition (DADA) acquisition function, which enforces data diversity to improve the prediction performance of a model. In this work, we employed a model simplification strategy (Network Auto-Reduction (NAR)) that significantly improves AL execution time when coupled with DADA. NAR produces less compute demanding models, which replace the target models during the AL process to reduce processing demands. An evaluation with a Tumor-Infiltrating Lymphocytes (TILs) classification application shows that: (i) DADA attains superior performance compared to state-of-the-art AL strategies for different Convolutional Neural Networks (CNNs), (ii) NAR improves the AL execution time by up to 4.3 ×, and (iii) target models trained with patches/data selected by the NAR reduced versions achieve similar or superior classification quality to using target CNNs for data selection.

AVAILABILITY : Source code: https://github.com/alsmeirelles/DADA.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Meirelles André L S, Kurc Tahsin, Kong Jun, Ferreira Renato, Saltz Joel, Teodoro George

2023-Mar-21

In Pain medicine (Malden, Mass.)

OBJECTIVES : To evaluate whether combining fast acquisitions with deep-learning reconstruction can provide diagnostically useful images and quantitative assessment comparable to standard-of-care acquisitions for lumbar spine MRI.

METHODS : Eighteen patients were imaged with both standard protocol and fast protocol using reduced signal averages, each protocol including sagittal fat-suppressed T2-weighted, sagittal T1-weighted, and axial T2-weighted 2D fast spin-echo sequences. Fast-acquisition data was additionally reconstructed using vendor-supplied deep-learning reconstruction with three different noise reduction factors. For qualitative analysis, standard images as well as fast images with and without deep-learning reconstruction were graded by three radiologists on five different categories. For quantitative analysis, convolutional neural networks were applied to sagittal T1-weighted images to segment intervertebral discs and vertebral bodies, and disc heights and vertebral body volumes were derived.

RESULTS : Based on noninferiority testing on qualitative scores, fast images without deep-learning reconstruction were inferior to standard images for most categories. However, deep-learning reconstruction improved the average scores, and noninferiority was observed over 24 out of 45 comparisons (all with sagittal T2-weighted images while 4/5 comparisons with sagittal T1-weighted and axial T2-weighted images). Interobserver variability increased with 50 and 75% noise reduction factors. Deep-learning reconstructed fast images with 50% and 75% noise reduction factors had comparable disc heights and vertebral body volumes to standard images (r2 ≥ 0.86 for disc heights and r2 ≥ 0.98 for vertebral body volumes).

CONCLUSIONS : This study demonstrated that deep-learning-reconstructed fast-acquisition images have the potential to provide noninferior image quality and comparable quantitative assessment to standard clinical images.

Han Misung, Bahroos Emma, Hess Madeline E, Chin Cynthia T, Gao Kenneth T, Shin David D, Villanueva-Meyer Javier E, Link Thomas M, Pedoia Valentina, Majumdar Sharmila

2023-Mar-21

Clinical MRI, Deep Learning Reconstruction, Fast Acquisition, Lower Back Pain, Lumbar Spine MRI, Segmentation

In Nature medicine ; h5-index 170.0

Watanabe Kengo, Wilmanski Tomasz, Diener Christian, Earls John C, Zimmer Anat, Lincoln Briana, Hadlock Jennifer J, Lovejoy Jennifer C, Gibbons Sean M, Magis Andrew T, Hood Leroy, Price Nathan D, Rappaport Noa

2023-Mar-20