AIMC Topic: Retrospective Studies

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HRProfiler Detects Homologous Recombination Deficiency in Breast and Ovarian Cancers Using Whole-Genome and Whole-Exome Sequencing Data.

Cancer research
UNLABELLED: Breast and ovarian cancers harboring homologous recombination deficiency (HRD) are sensitive to PARP inhibitors and platinum chemotherapy. Conventionally, detecting HRD involves screening for defects in BRCA1, BRCA2, and other relevant ge...

A supervised machine learning approach for predicting the need for postsurgical intervention in acromegaly.

Neurosurgical focus
OBJECTIVE: Patients with growth hormone (GH)-secreting pituitary adenomas (PAs) experience various symptoms and comorbidities, which can ultimately lead to increased mortality. This study aimed to develop and validate a machine learning (ML) model fo...

A novel deep learning system for automated diagnosis and grading of lumbar spinal stenosis based on spine MRI: model development and validation.

Neurosurgical focus
OBJECTIVE: The study aimed to develop a single-stage deep learning (DL) screening system for automated binary and multiclass grading of lumbar central stenosis (LCS), lateral recess stenosis (LRS), and lumbar foraminal stenosis (LFS).

A multiregional multimodal machine learning model for predicting outcome of surgery for symptomatic hemorrhagic brainstem cavernous malformations.

Neurosurgical focus
OBJECTIVE: Given that resection of brainstem cavernous malformations (BSCMs) ends hemorrhaging but carries a high risk of neurological deficits, it is necessary to develop and validate a model predicting surgical outcomes. This study aimed to constru...

Machine learning approaches for predicting prolonged hospital length of stay after lumbar fusion surgery in patients aged 75 years and older: a retrospective cohort study based on comprehensive geriatric assessment.

Neurosurgical focus
OBJECTIVE: Postoperative recovery following lumbar fusion surgery in patients aged 75 years and older often requires a prolonged length of stay (PLOS) in the hospital. Accurately predicting the risk of PLOS and assessing its risk factors for preopera...

Deep learning-based clinical decision support system for intracerebral hemorrhage: an imaging-based AI-driven framework for automated hematoma segmentation and trajectory planning.

Neurosurgical focus
OBJECTIVE: Intracerebral hemorrhage (ICH) remains a critical neurosurgical emergency with high mortality and long-term disability. Despite advancements in minimally invasive techniques, procedural precision remains limited by hematoma complexity and ...

Does alignment alone predict mechanical complications after adult spinal deformity surgery? A machine learning comparison of alignment, bone quality, and soft tissue.

Neurosurgical focus
OBJECTIVE: Mechanical complications are a vexing occurrence after adult spinal deformity (ASD) surgery. While achieving ideal spinal alignment in ASD surgery is critical, alignment alone may not fully explain all mechanical complications. The authors...

Image-based detection of the internal carotid arteries and sella turcica in endoscopic endonasal transsphenoidal surgery.

Neurosurgical focus
OBJECTIVE: Endoscopic endonasal transsphenoidal surgery (EETS) is a minimally invasive procedure that accesses the sellar and parasellar regions. Various anatomical structures must be identified during the operation, particularly the sella turcica an...

Deep Learning Models for CT Segmentation of Invasive Pulmonary Aspergillosis, Mucormycosis, Bacterial Pneumonia and Tuberculosis: A Multicentre Study.

Mycoses
BACKGROUND: The differential diagnosis of invasive pulmonary aspergillosis (IPA), pulmonary mucormycosis (PM), bacterial pneumonia (BP) and pulmonary tuberculosis (PTB) are challenging due to overlapping clinical and imaging features. Manual CT lesio...

[A deep learning method for differentiating nasopharyngeal carcinoma and lymphoma based on MRI].

Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery
To development a deep learning(DL) model based on conventional MRI for automatic segmentation and differential diagnosis of nasopharyngeal carcinoma(NPC) and nasopharyngeal lymphoma(NPL). The retrospective study included 142 patients with NPL and 292...