|
1
|
Boeckxstaens GE, Zaninotto G and Richter
JE: Achalasia. Lancet. 383:83–93. 2014.PubMed/NCBI View Article : Google Scholar
|
|
2
|
Vaezi MF, Pandolfino JE, Yadlapati RH,
Greer KB and Kavitt RT: ACG clinical guidelines: Diagnosis and
management of achalasia. Am J Gastroenterol. 115:1393–1411.
2020.PubMed/NCBI View Article : Google Scholar
|
|
3
|
Vaezi MF, Pandolfino JE and Vela MF: ACG
clinical guideline: Diagnosis and management of achalasia. Am J
Gastroenterol. 108:1238–1249, 1250. 2013.PubMed/NCBI View Article : Google Scholar
|
|
4
|
Naik RD, Vaezi MF, Gershon AA,
Higginbotham T, Chen JJ, Flores E, Holzman M, Patel D and Gershon
MD: Association of achalasia with active varicella zoster virus
infection of the esophagus. Gastroenterology. 161:719–721.e2.
2021.PubMed/NCBI View Article : Google Scholar
|
|
5
|
Gaber CE, Cotton CC, Eluri S, Lund JL,
Farrell TM and Dellon ES: Autoimmune and viral risk factors are
associated with achalasia: A case-control study. Neurogastroenterol
Motil. 34(e14312)2022.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Yamasaki T, Tomita T, Mori S, Takimoto M,
Tamura A, Hara K, Kondo T, Kono T, Tozawa K, Ohda Y, et al:
Esophagography in patients with esophageal achalasia diagnosed with
high-resolution esophageal manometry. J Neurogastroenterol Motil.
24:403–409. 2018.PubMed/NCBI View
Article : Google Scholar
|
|
7
|
Kahrilas PJ, Bredenoord AJ, Fox M, Gyawali
CP, Roman S, Smout AJ and Pandolfino JE: International High
Resolution Manometry Working Group. The Chicago Classification of
esophageal motility disorders, v3.0. Neurogastroenterol Motil.
27:160–174. 2015.PubMed/NCBI View Article : Google Scholar
|
|
8
|
do Carmo GC, de Assis Mota G, da Silva
Castro Perdoná G and de Oliveira RB: Integrated relaxation pressure
and its diagnostic ability may vary according to the conditions
used for HREM recording. Dysphagia. 39:746–756. 2024.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Cha B and Jung KW: Diagnosis of dysphagia:
High resolution manometry & EndoFLIP. Korean J Gastroenterol.
77:64–70. 2021.PubMed/NCBI View Article : Google Scholar : (In Korean).
|
|
10
|
Czako Z, Surdea-Blaga T, Sebestyen G,
Hangan A, Dumitrascu DL, David L, Chiarioni G, Savarino E and Popa
SL: Integrated relaxation pressure classification and probe
positioning failure detection in high-resolution esophageal
manometry using machine learning. Sensors (Basel).
22(253)2021.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Gong EJ: Integrated relaxation pressure
during swallowing: An Ever-changing metric. J Neurogastroenterol
Motil. 27:151–152. 2021.PubMed/NCBI View
Article : Google Scholar
|
|
12
|
Kou W, Carlson DA, Baumann AJ, Donnan E,
Luo Y, Pandolfino JE and Etemadi M: A deep-learning-based
unsupervised model on esophageal manometry using variational
autoencoder. Artif Intell Med. 112(102006)2021.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Surdea-Blaga T, Sebestyen G, Czako Z,
Hangan A, Dumitrascu DL, Ismaiel A, David L, Zsigmond I, Chiarioni
G, Savarino E, et al: Automated Chicago classification for
esophageal motility disorder diagnosis using machine learning.
Sensors (Basel). 22(5227)2022.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Luus F, Khan N and Akhalwaya I: Active
learning with tensorboard projector. arXiv.org, 2019.
|
|
15
|
Van der Maaten L and Hinton G: Visualizing
data using t-SNE. J Mach Learn Res. 9:2579–2605. 2008.
|
|
16
|
Kanamori K, Koyanagi K, Ozawa S, Yamamoto
M, Ninomiya Y, Yatabe K, Higuchi T and Tajima K: Multimodal therapy
for esophageal squamous cell carcinoma according to TNM staging in
Japan-a narrative review of clinical trials conducted by Japan
clinical oncology group. Ann Esophagus. 6(32)2023.
|
|
17
|
Koyanagi K, Kanamori K, Ninomiya Y, Yatabe
K, Higuchi T, Yamamoto M, Tajima K and Ozawa S: Progress in
multimodal treatment for advanced esophageal squamous cell
carcinoma: Results of multi-institutional trials conducted in
Japan. Cancers (Basel). 13(51)2020.PubMed/NCBI View Article : Google Scholar
|
|
18
|
Higuchi T, Shoji Y, Koyanagi K, Tajima K,
Kanamori K, Ogimi M, Yatabe K, Ninomiya Y, Yamamoto M, Kazuno A, et
al: Multimodal treatment strategies to improve the prognosis of
locally advanced thoracic esophageal squamous cell carcinoma: A
narrative review. Cancers (Basel). 15(10)2022.PubMed/NCBI View Article : Google Scholar
|
|
19
|
Bolger JC, Donohoe CL, Lowery M and
Reynolds JV: Advances in the curative management of oesophageal
cancer. Br J Cancer. 126:706–717. 2022.PubMed/NCBI View Article : Google Scholar
|
|
20
|
Yang H, Wang F, Hallemeier CL, Lerut T and
Fu J: Oesophageal cancer. Lancet. 404:1991–2005. 2024.PubMed/NCBI View Article : Google Scholar
|
|
21
|
Nishizawa T and Suzuki H: Long-term
outcomes of endoscopic submucosal dissection for superficial
esophageal squamous cell carcinoma. Cancers (Basel).
12(2849)2020.PubMed/NCBI View Article : Google Scholar
|
|
22
|
Averbukh LD and Tadros M: The role of
automatically generated Chicago classification in delayed achalasia
diagnosis. ACG Case Rep J. 7(e00345)2020.PubMed/NCBI View Article : Google Scholar
|
|
23
|
Müller M, Förschler S, Wehrmann T, Marini
F, Gockel I and Eckardt AJ: Atypical presentations and pitfalls of
achalasia. Dis Esophagus. 36(doad029)2023.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Richter JE: High-resolution manometry in
diagnosis and treatment of achalasia: Help or hype. Curr
Gastroenterol Rep. 16(420)2014.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Inoue H, Minami H, Kobayashi Y, Sato Y,
Kaga M, Suzuki M, Satodate H, Odaka N, Itoh H and Kudo S: Peroral
endoscopic myotomy (POEM) for esophageal achalasia. Endoscopy.
42:265–271. 2010.PubMed/NCBI View Article : Google Scholar
|
|
26
|
Schlottmann F and Patti MG: Esophageal
achalasia: Current diagnosis and treatment. Expert Rev
Gastroenterol Hepatol. 12:711–721. 2018.PubMed/NCBI View Article : Google Scholar
|
|
27
|
Sato H, Takahashi K, Mizuno KI, Hashimoto
S, Yokoyama J and Terai S: A clinical study of peroral endoscopic
myotomy reveals that impaired lower esophageal sphincter relaxation
in achalasia is not only defined by high-resolution manometry. PLoS
One. 13(e0195423)2018.PubMed/NCBI View Article : Google Scholar
|
|
28
|
Kim E, Yoo IK, Yon DK, Cho JY and Hong SP:
Characteristics of a subset of achalasia with normal integrated
relaxation pressure. J Neurogastroenterol Motil. 26:274–280.
2020.PubMed/NCBI View
Article : Google Scholar
|
|
29
|
Kawami N, Hoshino S, Hoshikawa Y,
Takenouchi N, Hanada Y, Tanabe T, Goto O, Kaise M and Iwakiri K:
Validity of the cutoff value for integrated relaxation pressure
used in the Starlet high-resolution manometry system. J Nippon Med
Sch. 86:322–326. 2020.PubMed/NCBI View Article : Google Scholar
|
|
30
|
Morley TJ, Mikulski MF, Rade M, Chalhoub
J, Desilets DJ and Romanelli JR: Per-oral endoscopic myotomy for
the treatment of non-achalasia esophageal dysmotility disorders:
Experience from a single high-volume center. Surg Endosc.
37:1013–1020. 2023.PubMed/NCBI View Article : Google Scholar
|
|
31
|
Rogers AB, Rogers BD and Gyawali CP:
Pathophysiology of achalasia. Ann Esophagus. 3(27)2020.
|
|
32
|
Rohof WOA and Bredenoord AJ: Chicago
classification of esophageal motility disorders: Lessons learned.
Curr Gastroenterol Rep. 19(37)2017.PubMed/NCBI View Article : Google Scholar
|
|
33
|
Yadlapati R, Kahrilas PJ, Fox MR,
Bredenoord AJ, Prakash Gyawali C, Roman S, Babaei A, Mittal RK,
Rommel N, Savarino E, et al: Esophageal motility disorders on
high-resolution manometry: Chicago classification version
4.0©. Neurogastroenterol Motil.
33(e14058)2021.PubMed/NCBI View Article : Google Scholar
|
|
34
|
Fox MR, Sweis R, Yadlapati R, Pandolfino
J, Hani A, Defilippi C, Jan T and Rommel N: Chicago classification
version 4.0© technical review: Update on standard
high-resolution manometry protocol for the assessment of esophageal
motility. Neurogastroenterol Motil. 33(e14120)2021.PubMed/NCBI View Article : Google Scholar
|
|
35
|
Huang G, Liu Z, Pleiss G, van der Maaten L
and Weinberger KQ: Convolutional networks with dense connectivity.
IEEE Trans Pattern Anal Mach Intell. 44:8704–8716. 2022.PubMed/NCBI View Article : Google Scholar
|
|
36
|
Popa SL, Surdea-Blaga T, Dumitrascu DL,
Chiarioni G, Savarino E, David L, Ismaiel A, Leucuta DC, Zsigmond
I, Sebestyen G, et al: Automatic diagnosis of high-resolution
esophageal manometry using artificial intelligence. J
Gastrointestin Liver Dis. 31:383–389. 2022.PubMed/NCBI View Article : Google Scholar
|
|
37
|
Kou W, Galal GO, Klug MW, Mukhin V,
Carlson DA, Etemadi M, Kahrilas PJ and Pandolfino JE: Deep
learning-based artificial intelligence model for identifying
swallow types in esophageal high-resolution manometry.
Neurogastroenterol Motil. 34(e14290)2022.PubMed/NCBI View Article : Google Scholar
|
|
38
|
He K, Zhang X, Ren S and Sun J: Deep
residual learning for image recognition. arXiv. [csCV]:770–778.
2015.
|
|
39
|
Kou W, Carlson DA, Baumann AJ, Donnan EN,
Schauer JM, Etemadi M and Pandolfino JE: A multi-stage machine
learning model for diagnosis of esophageal manometry. Artif Intell
Med. 124(102233)2022.PubMed/NCBI View Article : Google Scholar
|
|
40
|
Cohen S, Lipshutz W and Hughes W: Role of
gastrin supersensitivity in the pathogenesis of lower esophageal
sphincter hypertension in achalasia. J Clin Invest. 50:1241–1247.
1971.PubMed/NCBI View Article : Google Scholar
|