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Ayoub NF, Glicksman JT. Artificial Intelligence in Rhinology. Otolaryngol Clin North Am 2024; 57:831-842. [PMID: 38821734 DOI: 10.1016/j.otc.2024.04.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2024]
Abstract
Rhinology, allergy, and skull base surgery are fields primed for the integration and implementation of artificial intelligence (AI). The heterogeneity of the disease processes within these fields highlights the opportunity for AI to augment clinical care and promote personalized medicine. Numerous research studies have been published demonstrating the development and clinical potential of AI models within the field. Most describe in silico evaluation models without direct clinical implementation. The major themes of existing studies include diagnostic or clinical decisions support, clustering patients into specific phenotypes or endotypes, predicting post-treatment outcomes, and surgical planning.
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Affiliation(s)
- Noel F Ayoub
- Department of Otolaryngology-Head & Neck Surgery, Mass Eye and Ear/Harvard Medical School, Boston, MA, USA.
| | - Jordan T Glicksman
- Department of Otolaryngology-Head & Neck Surgery, Mass Eye and Ear/Harvard Medical School, Boston, MA, USA
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Hajikarimloo B, Sabbagh Alvani M, Koohfar A, Goudarzi E, Dehghan M, Hojjat SH, Hashemi R, Tos SM, Akhlaghpasand M, Habibi MA. Clinical Application of Artificial Intelligence in Prediction of Intraoperative Cerebrospinal Fluid Leakage in Pituitary Surgery: A Systematic Review and Meta-Analysis. World Neurosurg 2024:S1878-8750(24)01558-4. [PMID: 39265946 DOI: 10.1016/j.wneu.2024.09.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2024] [Revised: 09/02/2024] [Accepted: 09/03/2024] [Indexed: 09/14/2024]
Abstract
BACKGROUND Postoperative cerebrospinal fluid (CSF) leakage is the leading adverse event in transsphenoidal surgery. Intraoperative CSF (ioCSF) leakage is one of the most important predictive factors for postoperative CSF leakage. This systematic review and meta-analysis aimed to evaluate the effectiveness of artificial intelligence (AI) models in predicting ioCSF. METHODS Literature records were retrieved on June 13, 2024, using the relevant key terms without filters in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from the included studies were extracted. The quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software. RESULTS Our results demonstrate that the AI models achieved a pooled sensitivity of 93.4% (95% confidence interval [CI]: 74.8%-98.6%) and specificity of 91.7% (95% CI: 75%-97.6%). The subgroup analysis revealed that the pooled sensitivities in machine learning and deep learning were 86.2% (95% CI: 83%-88.8%) and 99% (95% CI: 93%-99%), respectively (P < 0.01). The subgroup analysis demonstrated a pooled specificity of 92.1% (95% CI: 63.1%-98.7%) for machine learning and 90.6% (95% CI: 78.2%-96.3%) for deep learning models (P = 0.87). The diagnostic odds ratio meta-analysis revealed an odds ratio 114.6 (95% CI: 17.6-750.9). The summary receiver operating characteristic curve demonstrated that the overall area under the curve of the studies was 0.955, which is a considerable performance. CONCLUSIONS AI models have demonstrated promising performance for predicting the ioCSF leakage in pituitary surgery and can optimize the treatment strategy.
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Affiliation(s)
- Bardia Hajikarimloo
- Department of Neurological Surgery, University of Virginia, Charlottesville, Virginia, USA
| | - Mohammadamin Sabbagh Alvani
- Student Research Committee Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Ehsan Goudarzi
- Department of Medicine, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mandana Dehghan
- Department of Neurosurgery, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Seyed Hesam Hojjat
- Department of Neurosurgery, North Khorasan University of Medical Sciences, Bojnurd, Iran
| | - Rana Hashemi
- Cardiovascular Surgery Research and Development Committee, Iran University of Medical Sciences, Tehran, Iran
| | - Salem M Tos
- Department of Neurological Surgery, University of Virginia, Charlottesville, Virginia, USA
| | | | - Mohammad Amin Habibi
- Department of Neurosurgery, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
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Behzadi F, Alhusseini M, Yang SD, Mallik AK, Germanwala AV. A Predictive Model for Intraoperative Cerebrospinal Fluid Leak During Endonasal Pituitary Adenoma Resection Using a Convolutional Neural Network. World Neurosurg 2024; 189:e324-e330. [PMID: 38876190 DOI: 10.1016/j.wneu.2024.06.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2024] [Accepted: 06/09/2024] [Indexed: 06/16/2024]
Abstract
BACKGROUND Cerebrospinal fluid (CSF) leak during endoscopic endonasal transsphenoidal surgery can lead to postoperative complications. The clinical and anatomic risk factors of intraoperative CSF leak are not well defined. We applied a two-dimensional (2D) convolutional neural network (CNN) machine learning model to identify risk factors from preoperative magnetic resonance imaging. METHODS All adults who underwent endoscopic endonasal transsphenoidal surgery at our institution from January 2007 to March 2023 who had accessible preoperative stereotactic magnetic resonance imaging were included. A retrospective classic statistical analysis was performed to identify demographic, clinical, and anatomic risk factors of intraoperative CSF leak. Stereotactic T2-weighted brain magnetic resonance imaging scans were used to train and test a 2D CNN model. RESULTS Of 220 included patients, 81 (36.8%) experienced intraoperative CSF leak. Among all preoperative variables, visual disturbance was the only statistically significant identified risk factor (P = 0.008). The trained 2D CNN model predicted CSF leak with 92% accuracy and area under receiver operating characteristic curve of 0.90 (sensitivity of 86% and specificity of 93%). Class activation mapping of this model revealed that anatomic regions of CSF flow were most important in predicting CSF leak. CONCLUSIONS Further review of the class activation mapping gradients revealed regions of the diaphragma sellae, clinoid processes, temporal horns, and optic nerves to have anatomic correlation to intraoperative CSF leak risk. Additionally, visual disturbances from anatomic compression of the optic chiasm were the only identified clinical risk factor. Our 2D CNN model can help a treating team to better anticipate and prepare for intraoperative CSF leak.
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Affiliation(s)
- Faraz Behzadi
- Department of Neurological Surgery, Loyola University Medical Center, Maywood, Illinois, USA
| | - Mohammad Alhusseini
- Departments of Radiology and Medical Imaging, Loyola University Medical Center, Maywood, Illinois, USA
| | - Seunghyuk D Yang
- Department of Neurological Surgery, Loyola University Medical Center, Maywood, Illinois, USA
| | - Atul K Mallik
- Departments of Radiology and Medical Imaging, Loyola University Medical Center, Maywood, Illinois, USA
| | - Anand V Germanwala
- Department of Neurological Surgery, Loyola University Medical Center, Maywood, Illinois, USA; Stritch School of Medicine, Loyola University Chicago, Maywood, Illinois, USA.
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Maroufi SF, Doğruel Y, Pour-Rashidi A, Kohli GS, Parker CT, Uchida T, Asfour MZ, Martin C, Nizzola M, De Bonis A, Tawfik-Helika M, Tavallai A, Cohen-Gadol AA, Palmisciano P. Current status of artificial intelligence technologies in pituitary adenoma surgery: a scoping review. Pituitary 2024; 27:91-128. [PMID: 38183582 DOI: 10.1007/s11102-023-01369-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/27/2023] [Indexed: 01/08/2024]
Abstract
PURPOSE Pituitary adenoma surgery is a complex procedure due to critical adjacent neurovascular structures, variations in size and extensions of the lesions, and potential hormonal imbalances. The integration of artificial intelligence (AI) and machine learning (ML) has demonstrated considerable potential in assisting neurosurgeons in decision-making, optimizing surgical outcomes, and providing real-time feedback. This scoping review comprehensively summarizes the current status of AI/ML technologies in pituitary adenoma surgery, highlighting their strengths and limitations. METHODS PubMed, Embase, Web of Science, and Scopus were searched following the PRISMA-ScR guidelines. Studies discussing the use of AI/ML in pituitary adenoma surgery were included. Eligible studies were grouped to analyze the different outcomes of interest of current AI/ML technologies. RESULTS Among the 2438 identified articles, 44 studies met the inclusion criteria, with a total of seventeen different algorithms utilized across all studies. Studies were divided into two groups based on their input type: clinicopathological and imaging input. The four main outcome variables evaluated in the studies included: outcome (remission, recurrence or progression, gross-total resection, vision improvement, and hormonal recovery), complications (CSF leak, readmission, hyponatremia, and hypopituitarism), cost, and adenoma-related factors (aggressiveness, consistency, and Ki-67 labeling) prediction. Three studies focusing on workflow analysis and real-time navigation were discussed separately. CONCLUSION AI/ML modeling holds promise for improving pituitary adenoma surgery by enhancing preoperative planning and optimizing surgical strategies. However, addressing challenges such as algorithm selection, performance evaluation, data heterogeneity, and ethics is essential to establish robust and reliable ML models that can revolutionize neurosurgical practice and benefit patients.
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Affiliation(s)
- Seyed Farzad Maroufi
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Neurosurgical Research Network (NRN), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Yücel Doğruel
- Department of Neurosurgery, Yeditepe University School of Medicine, Istanbul, Turkey
| | - Ahmad Pour-Rashidi
- Department of Neurosurgery, Sina Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Gurkirat S Kohli
- Department of Neurosurgery, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | | | - Tatsuya Uchida
- Department of Neurosurgery, Stanford University, Palo Alto, CA, USA
| | - Mohamed Z Asfour
- Department of Neurosurgery, Nasser Institute for Research and Treatment Hospital, Cairo, Egypt
| | - Clara Martin
- Department of Neurosurgery, Hospital de Alta Complejidad en Red "El Cruce", Florencio Varela, Buenos Aires, Argentina
| | | | - Alessandro De Bonis
- Department of Neurosurgery and Gamma Knife Radiosurgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Amin Tavallai
- Department of Pediatric Neurosurgery, Children's Medical Center Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Paolo Palmisciano
- Department of Neurological Surgery, University of California, Davis, 4860 Y Street, Suite 3740, Sacramento, CA, 95817, USA.
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Villalonga JF, Burroni M, Fabozzi GL, Solari D, Campero A, Cappabianca P, Cavallo LM. Guanti bianchi technique for resection of selected pituitary adenomas. BRAIN & SPINE 2023; 3:101724. [PMID: 37383463 PMCID: PMC10293224 DOI: 10.1016/j.bas.2023.101724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 02/17/2023] [Accepted: 02/22/2023] [Indexed: 06/30/2023]
Abstract
Introduction Since the introduction of the endoscopic endonasal approach (EEA) to skull base, the nasal phase has been a true challenge as it represents the moment of definition of the corridor, thus defining the instruments maneuverability at tumor removal phase. The longstanding cooperation between ENT and neurosurgeons have provided the possibility of creating adequate corridor with maximal respect toward nasal structures and mucosa. This sparked the idea of entering the sella as thieves, so we named "Guanti Bianchi" technique a lesser invasive variation of the approach for the removal of selected pituitary adenoma. Research Question The purpose of this study is to present the preliminary results of "Guanti Bianchi" technique. Material and Methods Data from 17 patients undergoing "Guanti Bianchi" technique (out of 235 standard EEA) at our center, were retrospectively analysed. ASK Nasal-12, a quality-of-life instrument developed specifically to assess patient perception of nasal morbidity, was administered pre- and postoperatively. Results 10 (59%) patients were men and 7 (41%) women. The mean age was 67.7 (range 35-88). The average duration of the surgical procedure was 71.17 minutes (range 45-100). GTR was achieved in all patients, no postoperative complications were observed. Baseline ASK Nasal-12 was near normal in all patients, 3/17 (17,6%) experienced transitory very mild symptoms without any worsening at 3 and 6 months. Discussion and Conclusions This minimally invasive technique does not require turbinectomy or carving of the nasoseptal flap, it alters the nasal mucosa as little as necessary, and it is quick and easy to perform.
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Affiliation(s)
- Juan F. Villalonga
- LINT, Facultad de Medicina, Universidad Nacional de Tucumán, Tucumán, Argentina
- Department of Neurological Surgery, Hospital Padilla, Tucumán, Argentina
| | - Matias Burroni
- Department of Neurosurgery, Hospital Pedro de Elizalde, Buenos Aires, Argentina
| | - Gianluca L. Fabozzi
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, Division of Neurosurgery, University of Napoli “Federico II”, Naples, Italy
| | - Domenico Solari
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, Division of Neurosurgery, University of Napoli “Federico II”, Naples, Italy
| | - Alvaro Campero
- LINT, Facultad de Medicina, Universidad Nacional de Tucumán, Tucumán, Argentina
- Department of Neurological Surgery, Hospital Padilla, Tucumán, Argentina
| | - Paolo Cappabianca
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, Division of Neurosurgery, University of Napoli “Federico II”, Naples, Italy
| | - Luigi M. Cavallo
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, Division of Neurosurgery, University of Napoli “Federico II”, Naples, Italy
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